Download or read book Applications of Bayesian Methods to the Prediction of Educational Performance written by Melvin R. Novick and published by . This book was released on 1971 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Applications of Bayesian Methods to the Prediction of Educational Performance written by and published by . This book was released on 1971 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bayesian Reasoning and Gaussian Processes for Machine Learning Applications written by Hemachandran K and published by CRC Press. This book was released on 2022-04-14 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas, such as game development, decision making, and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework. The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning, which is followed by Gaussian process introduction, classification, regression, covariance, and performance analysis of Gaussian processes with other models. FEATURES Contains recent advancements in machine learning Highlights applications of machine learning algorithms Offers both quantitative and qualitative research Includes numerous case studies This book is aimed at graduates, researchers, and professionals in the field of data science and machine learning.
Download or read book Bayesian Statistics and Its Applications written by Satyanshu K. Upadhyay and published by Anshan Pub. This book was released on 2007 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last two decades, Bayesian Statistics has acquired immense importance and has penetrated almost every area including those where the application of statistics appeared to be a remote possibility. This volume provides both theoretical and practical insights into the subject with detailed up-to-date material on various aspects. It serves two important objectives - to offer a thorough background material for theoreticians and gives a variety of applications for applied statisticians and practitioners. Consisting of 33 chapters, it covers topics on biostatistics, econometrics, reliability, image analysis, Bayesian computation, neural networks, prior elicitation, objective Bayesian methodologies, role of randomisation in Bayesian analysis, spatial data analysis, nonparametrics and a lot more. The book will serve as an excellent reference work for updating knowledge and for developing new methodologies in a wide variety of areas. It will become an invaluable tool for statisticians and the practitioners of Bayesian paradigm.
Download or read book Bayesian Networks in Educational Assessment written by Russell G. Almond and published by Springer. This book was released on 2015-03-10 with total page 678 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian inference networks, a synthesis of statistics and expert systems, have advanced reasoning under uncertainty in medicine, business, and social sciences. This innovative volume is the first comprehensive treatment exploring how they can be applied to design and analyze innovative educational assessments. Part I develops Bayes nets’ foundations in assessment, statistics, and graph theory, and works through the real-time updating algorithm. Part II addresses parametric forms for use with assessment, model-checking techniques, and estimation with the EM algorithm and Markov chain Monte Carlo (MCMC). A unique feature is the volume’s grounding in Evidence-Centered Design (ECD) framework for assessment design. This “design forward” approach enables designers to take full advantage of Bayes nets’ modularity and ability to model complex evidentiary relationships that arise from performance in interactive, technology-rich assessments such as simulations. Part III describes ECD, situates Bayes nets as an integral component of a principled design process, and illustrates the ideas with an in-depth look at the BioMass project: An interactive, standards-based, web-delivered demonstration assessment of science inquiry in genetics. This book is both a resource for professionals interested in assessment and advanced students. Its clear exposition, worked-through numerical examples, and demonstrations from real and didactic applications provide invaluable illustrations of how to use Bayes nets in educational assessment. Exercises follow each chapter, and the online companion site provides a glossary, data sets and problem setups, and links to computational resources.
Download or read book Bayesian Networks written by Olivier Pourret and published by John Wiley & Sons. This book was released on 2008-04-30 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis. This book provides a general introduction to Bayesian networks, defining and illustrating the basic concepts with pedagogical examples and twenty real-life case studies drawn from a range of fields including medicine, computing, natural sciences and engineering. Designed to help analysts, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips readers with proven methods to generate, calibrate, evaluate and validate Bayesian networks. The book: Provides the tools to overcome common practical challenges such as the treatment of missing input data, interaction with experts and decision makers, determination of the optimal granularity and size of the model. Highlights the strengths of Bayesian networks whilst also presenting a discussion of their limitations. Compares Bayesian networks with other modelling techniques such as neural networks, fuzzy logic and fault trees. Describes, for ease of comparison, the main features of the major Bayesian network software packages: Netica, Hugin, Elvira and Discoverer, from the point of view of the user. Offers a historical perspective on the subject and analyses future directions for research. Written by leading experts with practical experience of applying Bayesian networks in finance, banking, medicine, robotics, civil engineering, geology, geography, genetics, forensic science, ecology, and industry, the book has much to offer both practitioners and researchers involved in statistical analysis or modelling in any of these fields.
Download or read book Rational Descriptions Decisions and Designs written by Myron Tribus and published by Elsevier. This book was released on 2013-10-22 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rational Descriptions, Decisions and Designs is a reference for understanding the aspects of rational decision theory in terms of the basic formalism of information theory. The text provides ways to achieve correct engineering design decisions. The book starts with an understanding for the need to apply rationality, as opposed to uncertainty, in design decision making. Inductive logic in computers is explained where the design of the machine and the accompanying software are considered. The text then explains the functional equations and the problems of arriving at a rational description through some mathematical preliminaries. Bayes' equation and rational inference as tools for adjusting probabilities when something new is encountered in earlier probability distributions are explained. The book presents as well a case study concerning the error made in following specifications of spark plugs. The author also explains the Bernoulli trials, where a probability that a better hypothesis than that already adopted may exist. The rational measure of uncertainty and the principle of maximum entropy with sample calculations are included in the text. After considering the probabilities, the decision theory is taken up where engineering design follows. Examples regarding transmitter and voltmeter designs are presented. The book ends by explaining probabilities of success and failure as applied to reliability engineering, that it is a state of knowledge rather than the state of a thing. The text can serve as a textbook for students in technology engineering and design, and as a useful reference for mathematicians, statisticians, and fabrication engineers.
Download or read book Frontiers of Statistical Decision Making and Bayesian Analysis written by Ming-Hui Chen and published by Springer Science & Business Media. This book was released on 2010-07-24 with total page 631 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This book provides a review of current research challenges and opportunities. While the book can not exhaustively cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objective Bayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods for categorical and spatio-temporal data analysis and posterior simulation methods. Several major application areas are covered: computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modeling and applications in political science, finance and marketing. As a review of current research in Bayesian analysis the book presents a balance between theory and applications. The lack of a clear demarcation between theoretical and applied research is a reflection of the highly interdisciplinary and often applied nature of research in Bayesian statistics. The book is intended as an update for researchers in Bayesian statistics, including non-statisticians who make use of Bayesian inference to address substantive research questions in other fields. It would also be useful for graduate students and research scholars in statistics or biostatistics who wish to acquaint themselves with current research frontiers.
Download or read book Research in Education written by and published by . This book was released on 1974 with total page 974 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Resources in Education written by and published by . This book was released on 1995 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book 1977 National Science Foundation Authorization written by United States. Congress. House. Committee on Science and Technology. Subcommittee on Science, Research, and Technology and published by . This book was released on 1976 with total page 928 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Multilevel Analysis of Educational Data written by R. Darrell Bock and published by Elsevier. This book was released on 2014-06-28 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multilevel Analysis of Educational Data Bayesian methods Empirical Bayes Generalized least squares Profile likelihoods E-M algorithm Fisher scoring procedures Both educational and social science applications
Download or read book Consistent Bayesian Learning for Neural Network Models written by Sanket Rajendra Jantre and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian framework adapted for neural network learning, Bayesian neural networks, have received widespread attention and successfully applied to various applications. Bayesian inference for neural networks promises improved predictions with reliable uncertainty estimates, robustness, principled model comparison, and decision-making under uncertainty. In this dissertation, we propose novel theoretically consistent Bayesian neural network models and provide their computationally efficient posterior inference algorithms.In Chapter 2, we introduce a Bayesian quantile regression neural network assuming an asymmetric Laplace distribution for the response variable. The normal-exponential mixturere presentation of the asymmetric Laplace density is utilized to derive the Gibbs sampling coupled with Metropolis-Hastings algorithm for the posterior inference. We establish the posterior consistency under a misspecified asymmetric Laplace density model. We illustrate the proposed method with simulation studies and real data examples.Traditional Bayesian learning methods are limited by their scalability to large data and feature spaces due to the expensive inference approaches, however recent developments in variational inference techniques and sparse learning have brought renewed interest to this area. Sparse deep neural networks have proven to be efficient for predictive model building in large-scale studies. Although several works have studied theoretical and numerical properties of sparse neural architectures, they have primarily focused on the edge selection.In Chapter 3, we propose a sparse Bayesian technique using spike-and-slab Gaussian prior to allow for automatic node selection. The spike-and-slab prior alleviates the need of an ad-hoc thresholding rule for pruning. In addition, we adopt a variational Bayes approach to circumvent the computational challenges of traditional Markov chain Monte Carlo implementation. In the context of node selection, we establish the variational posterior consistency together with the layer-wise characterization of prior inclusion probabilities. We empirically demonstrate that our proposed approach outperforms the edge selection method in computational complexity with similar or better predictive performance.The structured sparsity (e.g. node sparsity) in deep neural networks provides low latency inference, higher data throughput, and reduced energy consumption. Alternatively, there is a vast albeit growing literature demonstrating shrinkage efficiency and theoretical optimality in linear models of two sparse parameter estimation techniques: lasso and horseshoe. In Chapter 4, we propose structurally sparse Bayesian neural networks which systematically prune excessive nodes with (i) Spike-and-Slab Group Lasso, and (ii) Spike-and-Slab Group Horseshoe priors, and develop computationally tractable variational inference We demonstrate the competitive performance of our proposed models compared to the Bayesian baseline models in prediction accuracy, model compression, and inference latency.Deep neural network ensembles that appeal to model diversity have been used successfully to improve predictive performance and model robustness in several applications. However, most ensembling techniques require multiple parallel and costly evaluations and have been proposed primarily with deterministic models. In Chapter 5, we propose sequential ensembling of dynamic Bayesian neural subnetworks to generate diverse ensemble in a single forward pass. The ensembling strategy consists of an exploration phase that finds high-performing regions of the parameter space and multiple exploitation phases that effectively exploit the compactness of the sparse model to quickly converge to different minima in the energy landscape corresponding to high-performing subnetworks yielding diverse ensembles. We empirically demonstrate that our proposed approach surpasses the baselines of the dense frequentist and Bayesian ensemble models in prediction accuracy, uncertainty estimation, and out-of-distribution robustness. Furthermore, we found that our approach produced the most diverse ensembles compared to the approaches with a single forward pass and even compared to the approaches with multiple forward passes in some cases.
Download or read book The Educational Testing Act of 1981 written by United States. Congress. House. Committee on Education and Labor. Subcommittee on Elementary, Secondary, and Vocational Education and published by . This book was released on 1982 with total page 1092 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Intelligent Systems Design and Applications written by Ajith Abraham and published by Springer. This book was released on 2019-04-11 with total page 1158 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights recent research on Intelligent Systems and Nature Inspired Computing. It presents 212 selected papers from the 18th International Conference on Intelligent Systems Design and Applications (ISDA 2018) and the 10th World Congress on Nature and Biologically Inspired Computing (NaBIC), which was held at VIT University, India. ISDA-NaBIC 2018 was a premier conference in the field of Computational Intelligence and brought together researchers, engineers and practitioners whose work involved intelligent systems and their applications in industry and the “real world.” Including contributions by authors from over 40 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of Computer Science and Engineering.
Download or read book Bayesian Methods for Interaction and Design written by John H. Williamson and published by Cambridge University Press. This book was released on 2022-08-25 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces Bayesian methods and their implementation in application ranging from pointing-based interfaces to modelling cognitive processes.
Download or read book Mathematical Modeling and Computational Predictions in Oncoimmunology written by Vladimir A. Kuznetsov and published by Frontiers Media SA. This book was released on 2024-06-06 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cancer is a complex adaptive dynamic system that causes both local and systemic failures in the patient. Cancer is caused by a number of gain-of-function and loss-of-function events, that lead to cells proliferating without control by the host organism over time. In cancer, the immune system modulates cancer cell population heterogeneity and plays a crucial role in disease outcomes. The immune system itself also generates multiple clones of different cell types, with some clones proliferating quickly and maturing into effector cells. By creating regulatory signals and their networks, and generating effector cells and molecules, the immune system recognizes and kills abnormal cells. Anti-cancer immune mechanisms are realized as multi-layer, nonlinear cellular and molecular interactions. A number of factors determine the outcome of immune system-tumor interactions, including cancer-associated antigens, immune cells, and host organisms.
