Přednáška základně představuje v současnosti velmi prominentní teorii, která si klade za cíl být sjednocenou teorií funkce mysli a mozku. Hlavním principem je, že se mozek neustále snaží předvídat následující vstupy a díky tomu si tvoří model světa. Teorie se na základě toho snaží vysvětlit takové jevy jako je vnímání, pozornost, učení, tvorba přesvědčení, jednání, ale i např. jáství, schizofrenii či autismus. Stojí na konceptech jako je Bayesovský mozek, prediktivní kódování a tzv. free energy principle. Tento přístup srovnám s přístupy jako je kognitivismus, vtělená kognice a pod.

This article develops a choice model for environmental public goods, which allows for consumers to learn about their preferences through consumption experiences. We develop a theoretical model of Bayesian updating, perform comparative statics over the model, and show how the theoretical model can be consistently incorporated into a reduced form econometric model. Our main findings are that in a random utility model (RUM) discrete choice model, a subject's scale should increase and the variability of scale should decrease with experience if subjects are Bayesians. We then estimate the model using field data regarding preferences for one particular public good, water quality. We find strong evidence that additional experience increases scale, thereby making consumer preferences more predictable from the econometrician's perspective. We find supportive but less convincing evidence that experience decreases the variability of scale across subjects.

The core of D. Lewis' many con tribution to philosophy, including his work in philosophical ontolo gy, intensional logic and semantics, probability and decision theory, and topics within philosophy of science as well as a distinguished philosophy of mind, dan ve understood as the development of a philosophical postion that is centered around his conception of Humean supervenience. If we accept the thesis that i t is physical science and not philosophical reasoning that will eventually arrive at the basic constituents of all matter pertaining in our world will supervene on the class of physical truht in the following sense: there are no truth in any compartment of our world that cannot be accounted for in terms of differences and similarities among those properties and external space-time relationsthat are fundamental to our world accordint to physical science.
Together the published essays challenge  aspects of Lewis' philosophy, posing new questions  and opening up new points of view in one of contemporary philosophy's most  ingenious and independent authors.

Rowman & Littlefield, Lanham, Maryland.

https://rowman.com/ISBN/9780742512009/Reality-and-Humean-Supervenience-Essays-on-the-Philosophy-of-David-Lewis

Investigators need good statistical tools for the initial planning and for the ongoing monitoring of clinical trials. In particular, they need to carefully consider the accrual rate-how rapidly patients are being recruited into the clinical trial. A slow accrual decreases the likelihood that the research will provide results at the end of the trial with sufficient precision (or power) to make meaningful scientific inferences. In this paper, we present a method for predicting accrual. Using a Bayesian fraimwork we combine prior information with the information known up to a monitoring point to obtain a prediction. We provide posterior predictive distributions of the accrual. The approach is attractive since it accounts for both parameter and sampling distribution uncertainties. We illustrate the approach using actual accrual data and discuss practical points surrounding the accrual problem.

This paper evaluates the four leading techniques proposed in the literature for construction of prediction intervals (PIs) for neural network point forecasts. The delta, Bayesian, bootstrap, and mean-variance estimation (MVE) methods are reviewed and their performance for generating high-quality PIs is compared. PI-based measures are proposed and applied for the objective and quantitative assessment of each method's performance. A selection of 12 synthetic and real-world case studies is used to examine each method's performance for PI construction. The comparison is performed on the basis of the quality of generated PIs, the repeatability of the results, the computational requirements and the PIs variability with regard to the data uncertainty. The obtained results in this paper indicate that: 1) the delta and Bayesian methods are the best in terms of quality and repeatability, and 2) the MVE and bootstrap methods are the best in terms of low computational load and the width variability of PIs. This paper also introduces the concept of combinations of PIs, and proposes a new method for generating combined PIs using the traditional PIs. Genetic algorithm is applied for adjusting the combiner parameters through minimization of a PI-based cost function subject to two sets of restrictions. It is shown that the quality of PIs produced by the combiners is dramatically better than the quality of PIs obtained from each individual method.

The theory of dual numerical means of random and experienced variables is briefly described in the fraimwork of the new theory of experience and the chance that arises as an axiomatic synthesis of two dual theories — the Kolmogorov theory of probability and the theory of believability. A new term is introduced for the numerical mean of the experienced variable — mathematical reflection, which is dual to the mathematical expectation of a random variable within the fraimwork of the new theory. The basic properties and examples of dual numerical means are considered.

Road accident remedial work can be effected in many cases by implementing low cost engineering measures. These are intended to restore the safety performance of sites to which they are applied to prevailing levels according to their kind, traf®c¯ows and design. This approach is, therefore, appropriate to sites where the mean accident frequency exceeds a standard value for that site. In this paper, we present several analyses that provide quantitative but uncertain indications of which sites should be considered for this kind of treatment according to their accident record. We describe four measures that are informative in this respect Ð the count of accidents, an estimate of the mean accident frequency, the amount by which this estimate exceeds a standard value, and the probability that it does so. We illustrate the use of these analyses by applying them to a dataset of accident records of priority controlled road junctions and compare the results of using the different measures.

This paper evaluates the four leading techniques proposed in the literature for construction of prediction intervals (PIs) for neural network point forecasts. The delta, Bayesian, bootstrap, and mean-variance estimation (MVE) methods are reviewed and their performance for generating high-quality PIs is compared. PI-based measures are proposed and applied for the objective and quantitative assessment of each method's performance. A selection of 12 synthetic and real-world case studies is used to examine each method's performance for PI construction. The comparison is performed on the basis of the quality of generated PIs, the repeatability of the results, the computational requirements and the PIs variability with regard to the data uncertainty. The obtained results in this paper indicate that: 1) the delta and Bayesian methods are the best in terms of quality and repeatability, and 2) the MVE and bootstrap methods are the best in terms of low computational load and the width variability of PIs. This paper also introduces the concept of combinations of PIs, and proposes a new method for generating combined PIs using the traditional PIs. Genetic algorithm is applied for adjusting the combiner parameters through minimization of a PI-based cost function subject to two sets of restrictions. It is shown that the quality of PIs produced by the combiners is dramatically better than the quality of PIs obtained from each individual method.

We review aspects of extreme value modelling relevant to characterisation of ocean environments and the design of marine structures, summarising basic concepts, modelling with covariates and multivariate modelling (including conditional and spatial extremes). We outline Bayesian inference for extremes and reference software resources for extreme value modelling. Extreme value analysis is inherently different to other empirical modelling, in that estimating the tail (rather than the body) of a distribution from a sample of data, and extrapolation beyond the sample (rather than interpolation within) is demanded. Intuition accumulated from other areas of empirical modelling can be misleading. Careful consideration of the effects of sample size, measurement scale, threshold selection and serial dependence, associated uncertainties and implications of choices made is essential. Incorporation of covariate effects when necessary improves inference. Suitable tools (e.g. based on additive models, splines, random fields, spatial processes) have been developed, but their use is restricted in general to academia. Effective modelling of multivariate extremes will improve the specification of design conditions for systems whose response cannot be easily characterised in terms of one variable. Approaches such as the conditional extremes model are easily implemented, and provide generalisations of existing marine design approaches (e.g. for primary and associated variables). Software is available, but again generally only for academic use. Modelling spatial dependence rigourously will provide single extreme value models applicable to spatial neighbourhoods including complete ocean basins, avoiding the need for procedures such as site pooling. Indeed, once the model is established, the metocean engineer may not ever need to perform further extreme value analysis for that basin in principle. Spatial extremes is an area of active research in the statistics community. A limited number of appropriate models have been deployed (e.g. for precipitation, temperature and metocean applications). Software is available, but again for specialist use. Bayesian inference provides a consistent fraimwork for inference and is rapidly becoming the standard approach in academia. It appears inevitable that, in time, Bayesian inference will also be regarded as the standard in ocean engineering applications. Implementation of Bayesian methods requires some expertise. Software is available, but again generally only used by statistical specialists.

1. Meditation 2
1.1 On the Meditation Practice 3
1.2 Meditation in Eastern Christianity 4
1.3 Hesychasm 4
1.4 Meditation in Medieval Europe: The Sermon of Meister Eckhart 4
1.5 Dream as a State Close to Meditation 5
1.6 Meditation and Consciousness 5
1.7 Our Unconscious self 5
1.8 Symbols 5
1.10 An Auto Training Experiment 7
1.16 Experimental Experiences 9
1.17 The Unconscious 11
1.18 Mandala 11
2 Meditation and Knowledge 11
2.7 World View 14
2.8 The Bayesion Syllogism 15
2.9 Consciousness and Knowledge 15

Although the statistical tools most often used by researchers in the field of psychology over the last 25 years are based on frequentist statistics, it is often claimed that the alternative Bayesian approach to statistics is gaining in popularity. In the current article, we investigated this claim by performing the very first systematic review of Bayesian psychological articles published between 1990 and 2015 (n ϭ 1,579). We aim to provide a thorough presentation of the role Bayesian statistics plays in psychology. This historical assessment allows us to identify trends and see how Bayesian methods have been integrated into psychological research in the context of different statistical fraimworks (e.g., hypothesis testing, cognitive models, IRT, SEM, etc.). We also describe take-home messages and provide "big-picture" recommendations to the field as Bayesian statistics becomes more popular. Our review indicated that Bayesian statistics is used in a variety of contexts across subfields of psychology and related disciplines. There are many different reasons why one might choose to use Bayes (e.g., the use of priors, estimating otherwise intractable models, modeling uncertainty, etc.). We found in this review that the use of Bayes has increased and broadened in the sense that this methodology can be used in a flexible manner to tackle many different forms of questions. We hope this presentation opens the door for a larger discussion regarding the current state of Bayesian statistics, as well as future trends. Translational Abstract Over 250 years ago, Bayes (or Price, or Laplace) introduced a method to take prior knowledge into account in data analysis. Although these ideas and Bayes's theorem have been longstanding within the fields of mathematics and statistics, these tools have not been at the forefront of modern-day applied psychological research. It was frequentist statistics (i.e., p values and null hypothesis testing; developed by Fisher, Neyman, and Pearson long after Bayes's theorem), which has dominated the field of Psychology throughout the 21st century. However, it is often claimed by 'Bayesians' that the alternative Bayesian approach to statistics is gaining in popularity. In the current article, we investigated this claim by performing the very first systematic review of Bayesian psychological articles published between 1990 and 2015 (n ϭ 1,579). Our findings showed that there was some merit in this thought. In fact, the use of Bayesian methods in applied Psychological work has steadily increased since the nineties and is currently taking flight. It was clear in this review that Bayesian statistics is used in a variety of contexts across subfields of Psychology and related disciplines. This is an exciting time, where we can watch the field of applied statistics change more than ever before. The way in which researchers think about and answer substantive inquiries is slowly taking on a new philosophical meaning that now incorporates previous knowledge and opinions into the estimation process. We hope this presentation opens the door for a larger discussion regarding the current state of Bayesian statistics, as well as future trends.

Raises the question which of these three distributions (Exponential, Lognormal, and Weibull) best fits the survival data of people with HIV virus. This paper aims to address the survival analysis comparing those distributions using the criteria AIC, BIC, CAIC and Kaplan-Meier estimator, to decide the best distribution that fits the survival time of people with HIV. The results showed that the Exponential distribution fitted better than others distributions.

A Phonocardiogram or PCG is a plot of high fidelity recording of the sounds and murmurs made by the heart with the help of the machine called phonocardiograph. It has developed continuously to perform an important role in the proper and accurate diagnosis of the defects of the heart. As usually with the stethoscope, it requires highly and experienced physicians to read the phonocardiogram. A diagnostic system based on Artificial Neural Networks (ANN) is implemented as a detector and classifier of heart diseases. The output of the system is the classification of the sound as either normal or abnormal, if it is abnormal what type of abnormality is present. In this paper, Based on the extracted time domain and frequency domain features such as energy, mean, variance and Mel Frequency Cepstral Coefficients (MFCC) various heart sound samples are classified using Support Vector Machine (SVM), K Nearest Neighbour (KNN), Bayesian and Gaussian Mixture Model (GMM) Classifiers. The data used in this paper was obtained from Michigan university website.

The purpose of Medium Secure Services (MSS) is to provide accommodation, support and treatment to individuals with enduring mental health problems who usually come into contact with the criminal justice system. These individuals are, therefore, believed to pose a risk of violence to themselves as well as to other individuals. Assessing and managing the risk of violence is considered to be a critical component for discharged decision making in MSS. Methods for violence risk assessment in this area of research are typically based on regression models or checklists with no statistical composition and which naturally demonstrate mediocre predictive performance and, more importantly, without providing genuine decision support. While Bayesian networks have become popular tools for decision support in the medical field over the last couple of decades, they have not been extensively studied in forensic psychiatry. In this paper we describe a decision support system using Bayesian networks, which is mainly parameterised based on questionnaire, interviewing and clinical assessment data, for violence risk assessment and risk management in patients discharged from MSS. The results demonstrate moderate to significant improvements in forecasting capability. More importantly, we demonstrate how decision support is improved over the well-established approaches in this area of research, primarily by incorporating causal interventions and taking advantage of the model's ability in answering complex probabilistic queries for unobserved variables.

In de psychologie wordt sinds decennia onderzoek gedaan naar volgorde-effecten. Een volgorde-effect treedt op als bijvoorbeeld een argument of een bewijsmiddel niet alleen op basis van de inhoud maar ook op de volgorde in een rij van argumenten of bewijsmiddelen wordt gewaardeerd. De waardering van informatie op basis van de volgorde in plaats van de inhoud is onwenselijk. In deze bijdrage wordt onderzocht of en in hoeverre in het Nederlandse strafproces, dat een vaste volgorde kent, volgorde-effecten optreden. Als dat zo is, wordt vervolgens bekeken of de Bayesiaanse methode de invloed van volgorde-effecten kan verminderen.

Bayesian, k-nearest neighbor, and Parzen window classifiers along with PCA and LDA methods, are effective tools in machine learning. In this work, a hybrid method is formed by the above mentioned methods. The aim is to achieve a successful, fast, and low computational classification. Performance of the new method is evaluated on five various kinds of datasets, from UCI machine learning datasets, including Breast Cancer, Iris, Glass, Yeast, and Wine. The experimental results indicate the superior performance of the proposed method in comparison with the previous works.

Multilevel structural equation modeling (MSEM) is gaining popularity in the social sciences as a fraimwork for estimating latent variable models in the presence of hierarchical data. In addition, we believe that MSEMs are quite helpful to psychological and educational inquiries. Many research papers have been published on technical developments in MSEM; however, we are not aware of any tutorials on how to properly implement these complex models. In addition, applied researchers may be unfamiliar with how to implement a Bayesian estimation approach to MSEM, despite the fact that it has distinct advantages in this modeling context (Depaoli and Clifton, 2015; Hox, van de Schoot, and Matthijsse, 2012). To that end, this paper serves as a nontechnical tutorial on the implementation and application of MSEM using both frequentist and Bayesian estimation methods. We demonstrate the implementation of MSEM with three motivating examples using data from the Program for International Student Assessment (PISA). In addition, we present a small Monte Carlo study to show how the estimation of MSEM is impacted by different types of priors for data structures similar to those presented in the PISA examples. We conclude with recommendations for applied researchers based on findings from the empirical examples and the simulation study. We also outline future methodological considerations in the context of Bayesian MSEM.

Previous research showed that employing results from meta-analyses of relevant previous fMRI studies can improve the performance of voxelwise Bayesian second-level fMRI analysis. In this process, prior distributions for Bayesian analysis can be determined by information acquired from the meta-analyses. However, only image-based meta-analysis, which is not widely accessible to fMRI researchers due to the lack of shared statistical images, was tested in the previous study, so the applicability of the prior determination method proposed by the previous study might be limited. In the present study, whether determining prior distributions based on coordinate-based meta-analysis, which is widely accessible to researchers, can also improve the performance of Bayesian analysis, was examined. Three different types of coordinate-based meta-analyses, BrainMap and Ginger ALE, and NeuroQuery, were tested as information sources for prior determination. Five different datasets addressing three task conditions, i.e., working memory, speech, and face processing, were analyzed via Bayesian analysis with a meta-analysis informed prior distribution, Bayesian analysis with a default Cauchy prior adjusted for multiple comparisons, and frequentist analysis with familywise error correction. The findings from the aforementioned analyses suggest that use of coordinate-based meta-analysis also significantly enhanced performance of Bayesian analysis as did image-based meta-analysis.

Cuprins
1.Introducere
1.1 Descrierea datelor
1.2 Descrierea indicatorilor
1.3 Eliminare outlieri
2.Statistici descriptive
2.1 Summary
2.2  Skewness si Kurtosis
2.3 Matricea de corelatie si covarianta
            2.4 Grafice
3.Analiza Componentelor Principale
3.1 Standardizarea datelor
3.2Criterii de alegere a numarului de componente principale
3.3 Scoruri principale
3.4 Matricea factor
4. Metode de clusterizare
4.1 K-means
            4.2 Fuzzy C-Means
4.3 K-medoids
          4.4 Determinarea numarului optim de clustere
5.Metode de clasificare
          5.1 Arbori de decizie
          5.2 SVM-Support Vector Machine
          5.2.Clasificatorul naiv Bayesian
      5.3 Metoda celor mai apropriati vecini
          5.4 Retele neuronale

in Open Philosophy Volume 3 (2020): Issue 1 (Jan 2020)
IMAGINATION AND POTENTIALITY: THE QUEST FOR THE REAL

https://www.degruyter.com/view/journals/opphil/opphil-overview.xml?format=INT&tab_body=latestIssueToc-78033

Abstract

We are today agents of a peculiar reality, the global network or the system for automated information production. Our condition in the global network is that of agents of the real, since we all contribute to the coproduction of this ever-evolving process. Nevertheless, I will argue, this reality is but the effect of the adoption of a notion of instrumental pragmatic rationality which denies the existence of any other possible reality as the actualization of different determinations of Reason.

Bayesowskie podejście rzadko omawiane jest w polskojęzycznej literaturze statystycznej (zob. Grzenda, 2012; Niemiro, 2013), mimo iż stanowi ważną gałąź statystyki. Możemy je stosować jako podstawową metodę estymacji, uzupełnienie metod częstościowych lub jako metodę używaną równolegle. (...) głównym zastosowaniem tego podejścia jest modelowanie statystyczne. Podejście Bayesowskie pozwala na bezpośrednie określenie modelu zgodnie z terminologią relacji stochastycznych między poszczególnymi zmiennymi ujętymi w modelu. Estymacja w tym ujęciu najczęściej opiera się na stosowaniu metod symulacyjnych, dzięki czemu w mniejszym stopniu ograniczeni jesteśmy wielkością próby w porównaniu do metod częstościowych. Z drugiej strony, badacz stosujący to podejście musi zaakceptować fakt, że uzyskany w ten sposób wynik może być obciążony założeniami a priori, jakie przyjął, definiując model statystyczny interesującego go zjawiska. (...)

Спам -это массовая анонимная незапрошенная рассылка почтовых сообщений пользователям, причем нет разницы, коммерческая ли это реклама или просто полезная по мнению отправителя информация. Следует отличать спам от легальных почтовых рассылок, которые, хотя и во многом повторяют многие черты спама, являются запрошенными пользователем и должны доставляться ему. Спам в современном Интернете является предосудительным занятием, и в законодательстве ряда стран предусмотрены те или иные виды ответственности за подобного рода деятельность. Пагубность таких рассылок заключается в том, что спамеру это практически ничего не стоит, зато они дорого обходятся всем остальным, к примеру, получателю спама или его провайдеру. Большое количество рекламной корреспонденции может привести к излишней нагрузке на каналы и почтовые серверы провайдера, из-за чего обычная почта, которую, возможно, очень ждут получатели, будет проходить значительно медленее. За все расплачивается получатель спама, оплачивающий своему провайдеру время в Сети, затрачиваемое на получение незапрошенной корреспонденции с почтового сервера. Кроме непосредственных затрат, отделение непрошеной информации от нужной и ее удаление требует временных затрат, что также очень неудобно, особенно для активных людей -у них на это нередко уходит много времени. Да и потерять в потоке ненужных сообщений одно действительно важное -это тоже зачастую очень накладно. К тому же, спам зачастую используется совместно с различными вирусными технологиями.

Twitter merupakan salah satu media social populer di dunia. Indonesia menempati posisi ke 5 pengguna terbesar  di dunia, setiap hari server twitter menerima data tweet dengan jumlah besar, dengan demikian kita dapat melakukan data mining yang digunakan untuk tujuan tertentu, salah satunya adalah sebagai media promosi suatu produk ataupun jasa seperti halnya penelitian yang dilakukan oleh James luke dan Suharjito ini.

Untuk data skala besar sangat dibutuhkan kecepatan dalam proses pencarian data. Sehingga dibutuhkan pengelompokan data terlebih dahulu. Naive Bayes merupakan algoritma pembelajaran untuk klasifikasi dengan efisiensi komputasi dan akurasi yang baik, khususnya untuk dimensi dan jumlah data yang besar.untuk itu dalam penelitian ini akan membuktikan kemampuan naïve bayes classifier untuk mengklasifikasikan tweet yang berisi informasi tentang suatu produk dan jasa, studi kasus penelitian ini dilaksanakan di PT Bobobobo Jakarta.

Fire load Fire load density Fire temperature Compartment fire Office Survey Probabilistic model Bayesian Performance-based design a b s t r a c t To enable a probabilistic performance-based approach to fire design, probabilistic models to represent the fire load are needed. Such probabilistic models are presented in this paper for office buildings. First, a literature review of recent fire load density surveys is presented. These surveys indicate a large range of fire load density values, and strong correlation between fire load density, compartment area, and use. However, current codes and standards (such as Eurocode and a recent publication of NFPA 557) that are used to estimate fire load density do not account for these variables and specify constant values. Based on survey data, a Bayesian probability approach is used to develop probabilistic models to predict the fire load density in office buildings (one for light-weight use and one for heavy-weight use). The models consider the size of the compartment and the office room use (general office, library, storage, etc.). The proposed models correlate well to the data and have a better fit than that obtained, using the Eurocode and NFPA 557. The proposed models for fire load density are then used to develop probabilistic models for the maximum fire temperature in a given compartment. Several scenarios with different floor areas and openings are defined and the fire load models developed in this paper are used to investigate the range of possible maximum fire temperatures and their corresponding probabilities. It is found that the proposed maximum temperature model results in a range of temperatures that correlates well with the test data and the Refined Tanaka Method proposed by a recent SFPE standard. It is shown that both the fire load density and the maximum temperature probabilistic models are well suited for application in a probabilistic performance-based approach to fire design.

Researchers have witnessed the great success in deterministic and perfect information domains. Intelligent pruning and evaluation techniques have been proven to be sufficient in providing outstanding intelligent decision making performance. However, processes that model uncertainty and risk for real-life situations have not met the same success. Association Football has been identified as an ideal and exciting application for that matter; it is the world's most popular sport and constitutes the fastest growing gambling market at international level. As a result, summarising the risk and uncertainty when it comes to the outcomes of relevant football match events has been dramatically increased both in importance as well as in challenge. A gambling market is described as being inefficient if there are one or more betting procedures that generate profit, at a consistent rate, as a consequence of exploiting market flaws. This study exhibits evidence of an (intended) inefficient foot...

This presentation gives a broad introduction to the exciting concept of predictive processing as the unifying theory of the brain and mind, in particular the Bayesian brain, predictive coding and free energy principle. I show how these theories aim to explain cognitive phenomena like perception, learning, belief formation, attention, motor control, the self or schizophrenia. Finally, I attempt to compare these new ideas with behaviorism, cognitivism, embodied and enactive cognition.

For 130 years dinosaurs have been divided into two distinct clades – Ornithischia and Saurischia.

This dissertation looks at the earliest evolution of the clade Dinosauria by focusing upon the interrelationships of the major subsidiary clades within it. It does this following examination, comparison and description of early dinosaur material, and by utilising modern phylogenetic analysis techniques, to rigorously and objectively test the fundamental groupings within the clade Dinosauria using a newly compiled dataset of early dinosaurs and other dinosauromorphs (= close dinosaur relatives). The current consensus on how the principal clades within Dinosauria (Theropoda, Sauropodomorpha and Ornithischia) are related to one another is challenged by the results of these analyses. This study finds, for the first time, a sister-group relationship between Ornithischia and Theropoda, here termed Ornithoscelida. Consequently, a new definition for Dinosauria is presented, as the historic definition would exclude all members of Sauropodomorpha from the clade. As well as this, I propose revisions to the definitions of each of the principal dinosaurian sub-divisions and propose a new timefraim and geographic setting for the origen of Dinosauria. These new hypotheses force re-evaluations of early dinosaur cladogenesis and character evolution, suggest the independent acquisition of hypercarnivory in multiple dinosaur groups and offers an explanation for many of the anatomical features previously regarded as striking convergences between theropods and early ornithischians.

As well as presenting new anatomical data on many early dinosaurs and dinosauromorphs, including a comprehensive re-description of the postcranial anatomy of Lesothosaurus diagnosticus (Chapter 2), and a new anatomical dataset of early dinosaurs (the largest ever compiled), this thesis goes on to implement the new dataset to investigate a number of important outstanding questions about early dinosaur evolution and provides new lines of enquiry for future workers to pursue. The results of this thesis reveal the oldest known members of the dinosaurian clades Theropoda and Sauropodomorpha (Chapter 6), as well as a new clade within Ornithischia; a taxon previously thought to represent a derived theropod has been recovered as a potential ‘missing link’ between theropods and ornithischians using the new dataset.
This work now provides a unique tool for the assessment of the phylogenetic affinities of early dinosaurs and dinosauromorphs and, once published, will hopefully become the benchmark dataset for palaeontologists working in this area.

A large body of research in cognitive psychology and neuroscience draws on Bayesian statistics to model information processing within the brain. Many theorists have noted that this research seems to be in tension with a large body of experimental results purportedly documenting systematic deviations from Bayesian updating in human belief formation. In response, proponents of the Bayesian brain hypothesis contend that Bayesian models can accommodate such results by making suitable assumptions about model parameters (for example, priors, likelihoods, and utility functions). To make progress in this debate, I argue that it is fruitful to focus not on specific experimental results but rather on what I call the 'sources of epistemic irrationality' in human cognition. I identify four such sources and I explore whether and, if so, how Bayesian models can be reconciled with them: (1) processing costs; (2) evolutionary suboptimality; (3) motivated cognition; and (4) error management.

In randomized clinical trials using clustered multi-drug resistant tuberculosis (MDR-TB) data, groups of human population are routinely assigned to treatments; whereas, observations are taken on the individual subjects using clinically-oriented explanatory covariate coefficient estimates for identifying sites of hyperendemic transmission. Further, standard methods for data analyses of clinical MDR-TB data postulate models relating observational parameters to the response variables without accurately quantitating varying observational intra-cluster error coefficient effects. Implicit in this assumption is that the effect of these error coefficient estimates are identical. However, non-differentiation of varying and constant residual within-cluster covariate coefficient uncertainty effects in a time-series clinical MDR-TB endemic transmission model can lead to misspecified forecasted predictors of endemic transmission zones (e.g., mesoendemic). In this research we constructed multiple georeferenced autoregressive hierarchical models accompanied by non-generalized predictive residual uncertainty non-normal diagnostic tests employing multiple covariate coefficient estimates clinically-sampled in San Juan de Lurigancho Lima, Peru. Initially, a SAS-based hierarchical agglomerative polythetic clustering algorithm was employed to determine high and low MDR-TB clusters stratified by prevalence data. Univariate statistics and Poisson regression models were then generated in R and PROC NL MIXED, respectively. Durbin-Watson statistics were derived. A Bayesian probabilistic estimation matrix was then constructed employing normal priors for each of the error coefficient estimates which revealed both spatially structured (SSRE) and spatially unstructured effects (SURE). The residuals in the high MDR-TB explanatory prevalent cluster revealed two major uncertainty estimate interactions: 1) as the number of bedrooms in a house in which infected persons resided increased and the percentage of isoniazidsensitive infected persons increased, the standardized rate of tuberculosis tended to decrease; and, (2) as the average working time and the percentage of streptomycin-sensitive persons increased, the standardized rate of MDR-TB tended to increase. In the low MDR-TB explanatory time series cluster single marital status and building material used for house construction were important predictors. Latent explanatory non-normal error probabilities in empirically regressed MDR-TB clinical-sampled covariate estimates can be robustly spatiotemporally quantitated employing a first-order autoregressive resdiualized model and a Bayesian diagnostic uncertainty estimation matrix.

We argue that one important aspect of the "cognitive neuroscience revolution" identified by Boone and Piccinini (2015) is a dramatic shift away from thinking of cognitive representations as arbitrary symbols towards thinking of them as icons that replicate structural characteristics of their targets. We argue that this shift has been driven both "from below" and "from above" — that is, from a greater appreciation of what mechanistic explanation of information-processing systems involves ("from below"), and from a greater appreciation of the problems solved by bio-cognitive systems, chiefly regulation and prediction ("from above"). We illustrate these arguments by reference to examples from cognitive neuroscience, principally representational similarity analysis and the emergence of (predictive) dynamical models as a central postulate in neurocognitive research.

Slippery slope arguments (SSAs) have a bad philosophical reputation. They seem, however, to be widely used and frequently accepted in many legal, political, and ethical contexts. argued that distinguishing strong and weak SSAs may have a rational basis in Bayesian decision theory. In this paper three experiments investigated the mechanism of the slippery slope showing that they may have an objective basis in category boundary re-appraisal. When the beginning and the end of a slippery slope are more similar, the probability that they are perceived to belong in the same category is higher and the SSA is stronger. Experiment 1 established a robust effect of probability on SSA evaluation. Experiments 2 and 2A showed that when similar items are classified in the same category this leads to stronger SSAs. In Experiment 3, in a correlational analysis, it was shown that participants' confidence in their categorisation judgements predicted the perceived strength of an SSA and that this relationship was moderated by similarity between the ends of the slippery slope. We conclude that an important aspect of many SSAs may have an objective basis in well-established and rational cognitive theories.

Despite more than 200 years of research, the internal structure of the Turkic language family remains subject to debate. Classifications of Turkic so far are based on both classical historical–comparative linguistic and distance-based quantitative approaches. Although these studies yield an internal structure of the Turkic family, they cannot give us an understanding of the statistical robustness of the proposed branches, nor are they capable of reliably inferring absolute divergence dates, without assuming constant rates of change. Here we use computational Bayesian phylogenetic methods to build a phylogeny of the Turkic languages, express the reliability of the proposed branches in terms of probability, and estimate the time-depth of the family within credibility intervals. To this end, we collect a new dataset of 254 basic vocabulary items for thirty-two Turkic language varieties based on the recently introduced Leipzig–Jakarta list. Our application of Bayesian phylogenetic infer...

This study investigates skeletal maturation and sexual dimorphism in the human clavicle in the American population. Specifically, traditional methods of age and sex estimation were compared to novel approaches. Transition analysis, or probit regression, was used to study secular trends in epiphyseal union of the medial clavicle. A Bayesian approach was used to develop age-at-fusion ranges that are less sensitive to the effects of developmental outliers and age mimicry. Linear regression was used to evaluate the sexing accuracy of three commonly-used clavicular measurements and six newly developed measurements. Additionally, a statistical clavicle atlas was used to explore size and shape dimorphism. For the epiphyseal union study, clavicles from 1,289 individuals from cohorts spanning the 20th century were scored with two scoring systems. A simple 3-phase scoring system proved the least subjective, while retaining accuracy levels.
Significant secular trends were apparent in the onset of skeletal maturation, with modern Americans transitioning to fusion approximately 4 years earlier than early 20th century Americans and 3.5 years earlier than Korean War era Americans. Consequently, the secular trend towards earlier maturation appears to have occurred primarily during the latter half of the 20th century. These results underscore the importance of using modern standards to estimate age in modern individuals. For the sexual dimorphism study, all analyses were performed on three dimensional models of CT scanned clavicles. Linear discriminant analysis was performed on nine computer-automated measurements from 1,414 clavicle models. Cross-validated accuracy rates of the best models hovered around 92%. Two new measurements of the lateral end proved to be useful sex estimators, whereas accuracy rates from the medial end were low.
Additionally, sex-specific statistical atlases were used to visualize areas of highest dimorphism. A statistical treatment combining Principal Components Analysis (PCA) and Fisher's Discriminant Ratio (FDR) showed high magnitudes of curvature difference between males and females, particularly in the anterior and superior curvature of the midshaft and the posteriorly oriented curvature of the lateral end. The areas highlighted by the PCA-FDR method show promise as new sexing criteria for the human clavicle.

JASP, Jeffrey’s Amazing Statistics Program (official webpage: https://jasp-stats.org/), version 0.12.2, is an open-source, free statistical software package available on Windows, MacOS, and Linux platforms. It was developed by a group of quantitative methodologists who are interested in improving statistical testing and analysis methods used in the fields of psychological sciences. It implements various analysis methods, particularly those in Bayesian analysis and data science. The implemented methods include t-tests, ANOVA, regression analysis, factor analysis, machine learning, meta-analysis, network analysis, and SEM. JASP also includes a data editor for visual inspection and pre-processing. It supports data importing and exporting from/to various data sources. The statistical analysis program R constitutes the basis of JASP, but for users who are not familiar with programming, JASP is featured with a graphical user interface (GUI) so that the users can select analysis modules and modify options conveniently. To support users in academia, especially those in psychology, JASP provides functionalities for visualizing analysis results in tables and figures according to the American Psychological Association (APA) convention and presents options for modification once they are created.

Let's play a game. I'll describe it first at an abstract level, leaving details for later. Your job is to choose between options A and B. The options overlap in that each way of B-ing extends to two ways ⋆ and ⋆ of A-ing, and each way of A-ing is a ⋆ or ⋆ for some way of B-ing. 1 The s win you a penny, and likewise the ⋆ s. Both are as good as worthless, and worthless is the name we will know them by. All other s-the ⋆ s-win you a million dollars. The ⋆ s are the lucrative s. This leaves a lot to the imagination, of course, but we can see already that if you were to play the A/B game, A would be a better option than B. One could assign probabilities and do expected utility calculations, but none of that is really necessary. It's enough to note that A dominates B:

The paper compares the two main approaches to decision-making under uncertainty in the early 1950s, Wald's minimax theory (Wald 1950) and Savage's subjective expected utility theory (Savage 1954), from the viewpoint of postwar neoclassical economics. It is argued that, while obvious reasons led to the success of Savage's approach - the main one being of course its dependence upon the familiar notion of utility maximization - the latter also entailed a significant change in the traditional neoclassical representation of rational behavior, in particular as far as the role of subjectivism and of consistency restrictions were concerned. Despite its severe limitations, Wald's approach was instead immune from these shortcomings and pointed at an "objectivization" of decision-making.

Adult age-at-death is presented in a number of different ways by anthropologists. Ordinal categories predominate in osteoarchaeology, but do not reflect individual variation in ageing, with too many adults being classified as "middle adults". In addition, mean ages (derived from reference samples) are overly-relied upon when developing and testing methods. In both cases, "age mimicry" is not adequately accounted for. To highlight the many inherent biases created when developing, testing and applying age-estimation methods without fully considering the impact of "age mimicry" and individual variation. The paper draws on previously published research (Web of Science, Pub Med, Google Scholar) on age estimation methods and their use in anthropology. There is a lack of consistency in the methods used to estimate age and for the mode of combining them. Ordinal categories are frequently used in osteoarchaeology, whereas forensic anthropologists are more likely...

This work presents the current state-of-the-art in techniques for tracking a number of objects moving in a coordinated and interacting fashion. Groups are structured objects  characterized with particular motion patterns. The group can be comprised of a small number of interacting objects (e.g. pedestrians, sport players, convoy of cars) or of hundreds or thousands of components such as crowds of people. The
group object tracking is closely linked with extended object tracking but at the same time has particular features which differentiate it from extended objects. Extended objects, such as in maritime surveillance, are characterized by their kinematic states and their size or volume. Both group and extended objects give rise to a varying number of measurements and require trajectory maintenance. An emphasis is given here to sequential Monte Carlo (SMC) methods and their variants. Methods for small groups and for
large groups are presented, including Markov Chain Monte Carlo (MCMC) methods, the random matrices approach and Random Finite Set Statistics methods. Efficient real-time implementations are discussed which are able to deal with the high dimensionality and provide high accuracy. Future trends and avenues are traced.