Tags
Language
Tags
March 2024
Su Mo Tu We Th Fr Sa
25 26 27 28 29 1 2
3 4 5 6 7 8 9
10 11 12 13 14 15 16
17 18 19 20 21 22 23
24 25 26 27 28 29 30
31 1 2 3 4 5 6

"Bayesian Inference: Recent Trends" ed. by İhsan Bucak

Posted By: exLib
"Bayesian Inference: Recent Trends" ed. by İhsan Bucak

"Bayesian Inference: Recent Trends" ed. by İhsan Bucak
ITexLi | 2024 | ISBN: 1837693552 9781837693559 1837693560 9781837693566 1837693579 9781837693573 | 78 pages | PDF | 6 MB

This book is an invaluable resource for anyone interested in the intersection of statistics, machine learning, and data science. It offers a unique perspective on Bayesian inference, revealing its potential to provide robust solutions in an increasingly data-driven world. The book is your gateway to understanding and leveraging the power of Bayesian methods in the ever-evolving landscape of data analysis.

Introductory Statistical Inference with the Likelihood Function

Posted By: AvaxGenius
Introductory Statistical Inference with the Likelihood Function

Introductory Statistical Inference with the Likelihood Function by Charles A. Rohde
English | PDF (True) | 2014 | 341 Pages | ISBN : 3319104608 | 2.5 MB

This textbook covers the fundamentals of statistical inference and statistical theory including Bayesian and frequentist approaches and methodology possible without excessive emphasis on the underlying mathematics. This book is about some of the basic principles of statistics that are necessary to understand and evaluate methods for analyzing complex data sets. The likelihood function is used for pure likelihood inference throughout the book. There is also coverage of severity and finite population sampling. The material was developed from an introductory statistical theory course taught by the author at the Johns Hopkins University’s Department of Biostatistics. Students and instructors in public health programs will benefit from the likelihood modeling approach that is used throughout the text. This will also appeal to epidemiologists and psychometricians. After a brief introduction, there are chapters on estimation, hypothesis testing, and maximum likelihood modeling. The book concludes with sections on Bayesian computation and inference. An appendix contains unique coverage of the interpretation of probability, and coverage of probability and mathematical concepts.

Introduction to Bayesian Tracking and Particle Filters

Posted By: AvaxGenius
Introduction to Bayesian Tracking and Particle Filters

Introduction to Bayesian Tracking and Particle Filters by Lawrence D. Stone , Roy L. Streit , Stephen L. Anderson
English | PDF EPUB (True) | 2023 | 124 Pages | ISBN : 303132241X | 26.3 MB

This book provides a quick but insightful introduction to Bayesian tracking and particle filtering for a person who has some background in probability and statistics and wishes to learn the basics of single-target tracking. It also introduces the reader to multiple target tracking by presenting useful approximate methods that are easy to implement compared to full-blown multiple target trackers.

Bayesian Real-Time System Identification: From Centralized to Distributed Approach

Posted By: AvaxGenius
Bayesian Real-Time System Identification: From Centralized to Distributed Approach

Bayesian Real-Time System Identification: From Centralized to Distributed Approach by Ke Huang , Ka-Veng Yuen
English | PDF,EPUB | 2023 | 286 Pages | ISBN : 9819905923 | 90.6 MB

This book introduces some recent developments in Bayesian real-time system identification. It contains two different perspectives on data processing for system identification, namely centralized and distributed. A centralized Bayesian identification framework is presented to address challenging problems of real-time parameter estimation, which covers outlier detection, system, and noise parameters tracking. Besides, real-time Bayesian model class selection is introduced to tackle model misspecification problem. On the other hand, a distributed Bayesian identification framework is presented to handle asynchronous data and multiple outlier corrupted data. This book provides sufficient background to follow Bayesian methods for solving real-time system identification problems in civil and other engineering disciplines. The illustrative examples allow the readers to quickly understand the algorithms and associated applications. This book is intended for graduate students and researchers in civil and mechanical engineering. Practitioners can also find useful reference guide for solving engineering problems.

"Bayesian Inference: Recent Advantages" ed. by Niansheng Tang

Posted By: exLib
"Bayesian Inference: Recent Advantages" ed. by Niansheng Tang

"Bayesian Inference: Recent Advantages" ed. by Niansheng Tang
ITexLi | 2022 | ISBN: 1803560452 9781803560458 1803560444 9781803560441 1803560460 9781803560465 | 109 pages | PDF | 8 MB

This book introduces recent developments in Bayesian inference, and covers a variety of topics including robust Bayesian estimation, solving inverse problems via Bayesian theories, hierarchical Bayesian inference, and its applications for scattering experiments. The book will stimulate more extensive research on Bayesian fronts to include theories, methods, computational algorithms and applications in various fields such as data science, AI, machine learning, and causality analysis.

Decision Making Under Uncertainty and Reinforcement Learning: Theory and Algorithms

Posted By: AvaxGenius
Decision Making Under Uncertainty and Reinforcement Learning: Theory and Algorithms

Decision Making Under Uncertainty and Reinforcement Learning: Theory and Algorithms by Christos Dimitrakakis , Ronald Ortner
English | PDF,EPUB | 2022 | 251 Pages | ISBN : 3031076125 | 22.6 MB

This book presents recent research in decision making under uncertainty, in particular reinforcement learning and learning with expert advice. The core elements of decision theory, Markov decision processes and reinforcement learning have not been previously collected in a concise volume. Our aim with this book was to provide a solid theoretical foundation with elementary proofs of the most important theorems in the field, all collected in one place, and not typically found in
introductory textbooks. This book is addressed to graduate students that are interested in statistical decision making under uncertainty and the foundations of reinforcement learning.

Data Assimilation Fundamentals: A Unified Formulation of the State and Parameter Estimation Problem

Posted By: AvaxGenius
Data Assimilation Fundamentals: A Unified Formulation of the State and Parameter Estimation Problem

Data Assimilation Fundamentals: A Unified Formulation of the State and Parameter Estimation Problem by Geir Evensen
English | PDF,EPUB | 2022 | 251 Pages | ISBN : 3030967085 | 55.7 MB

This textbook's significant contribution is the unified derivation of data-assimilation techniques from a common fundamental and optimal starting point, namely Bayes' theorem. Unique for this book is the "top-down" derivation of the assimilation methods. It starts from Bayes theorem and gradually introduces the assumptions and approximations needed to arrive at today's popular data-assimilation methods.

The 40th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering

Posted By: AvaxGenius
The 40th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering

The 40th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering by Wolfgang von der Linden
English | PDF | 2022 | 144 Pages | ISBN : N/A | 5.7 MB

This workshop series was initiated by Myron Tribus and Edwin T. Jaynes, and organised for the first time by C.Ray Smith and Walter T. Grandy in 1981 at the University in Wyoming. Since then, tremendous progress has been made and the number of publications based on Bayesian methods is impressive

Bayesian Computation with R (Repost)

Posted By: AvaxGenius
Bayesian Computation with R (Repost)

Bayesian Computation with R by Jim Albert
English | PDF | 2009 | 304 Pages | ISBN : 0387922970 | 3.2 MB

There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry.

An Introduction to Bayesian Inference, Methods and Computation

Posted By: AvaxGenius
An Introduction to Bayesian Inference, Methods and Computation

An Introduction to Bayesian Inference, Methods and Computation by Nick Heard
English | PDF,EPUB | 2021 | 176 Pages | ISBN : 3030828077 | 38.2 MB

These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy and principles of Bayesian inference, including the reasoning behind the prior/likelihood model construction synonymous with Bayesian methods, through to advanced topics such as nonparametrics, Gaussian processes and latent factor models. These advanced modelling techniques can easily be applied using computer code samples written in Python and Stan which are integrated into the main text. Importantly, the reader will learn methods for assessing model fit, and to choose between rival modelling approaches.

Sampling Theory and Practice

Posted By: AvaxGenius
Sampling Theory and Practice

Sampling Theory and Practice by Changbao Wu
English | PDF,EPUB | 2020 | 371 Pages | ISBN : 3030442446 | 15.1 MB

The three parts of this book on survey methodology combine an introduction to basic sampling theory, engaging presentation of topics that reflect current research trends, and informed discussion of the problems commonly encountered in survey practice.