Non-stratified Chain Event Graphs
Author | : Aditi Shenvi |
Publisher | : |
Total Pages | : 0 |
Release | : 2021 |
Genre | : Bayesian statistical decision theory |
ISBN | : |
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Author | : Aditi Shenvi |
Publisher | : |
Total Pages | : 0 |
Release | : 2021 |
Genre | : Bayesian statistical decision theory |
ISBN | : |
Author | : Rodrigo A. Collazo |
Publisher | : CRC Press |
Total Pages | : 255 |
Release | : 2018-01-29 |
Genre | : Business & Economics |
ISBN | : 1498729614 |
Written by some major contributors to the development of this class of graphical models, Chain Event Graphs introduces a viable and straightforward new tool for statistical inference, model selection and learning techniques. The book extends established technologies used in the study of discrete Bayesian Networks so that they apply in a much more general setting As the first book on Chain Event Graphs, this monograph is expected to become a landmark work on the use of event trees and coloured probability trees in statistics, and to lead to the increased use of such tree models to describe hypotheses about how events might unfold. Features: introduces a new and exciting discrete graphical model based on an event tree focusses on illustrating inferential techniques, making its methodology accessible to a very broad audience and, most importantly, to practitioners illustrated by a wide range of examples, encompassing important present and future applications includes exercises to test comprehension and can easily be used as a course book introduces relevant software packages Rodrigo A. Collazo is a methodological and computational statistician based at the Naval Systems Analysis Centre (CASNAV) in Rio de Janeiro, Brazil. Christiane Görgen is a mathematical statistician at the Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany. Jim Q. Smith is a professor of statistics at the University of Warwick, UK. He has published widely in the field of statistics, AI, and decision analysis and has written two other books, most recently Bayesian Decision Analysis: Principles and Practice (Cambridge University Press 2010).
Author | : Guy Freeman |
Publisher | : |
Total Pages | : 356 |
Release | : 2010 |
Genre | : |
ISBN | : |
Author | : |
Publisher | : |
Total Pages | : 394 |
Release | : 2008 |
Genre | : |
ISBN | : |
Author | : Raffaele Argiento |
Publisher | : Springer Nature |
Total Pages | : 184 |
Release | : 2019-11-21 |
Genre | : Mathematics |
ISBN | : 3030306119 |
This book presents a selection of peer-reviewed contributions to the fourth Bayesian Young Statisticians Meeting, BAYSM 2018, held at the University of Warwick on 2-3 July 2018. The meeting provided a valuable opportunity for young researchers, MSc students, PhD students, and postdocs interested in Bayesian statistics to connect with the broader Bayesian community. The proceedings offer cutting-edge papers on a wide range of topics in Bayesian statistics, identify important challenges and investigate promising methodological approaches, while also assessing current methods and stimulating applications. The book is intended for a broad audience of statisticians, and demonstrates how theoretical, methodological, and computational aspects are often combined in the Bayesian framework to successfully tackle complex problems.
Author | : Alejandra Avalos-Pacheco |
Publisher | : Springer Nature |
Total Pages | : 119 |
Release | : 2024-01-06 |
Genre | : Mathematics |
ISBN | : 3031424131 |
This book hosts the results presented at the 6th Bayesian Young Statisticians Meeting 2022 in Montréal, Canada, held on June 22–23, titled "Bayesian Statistics, New Generations New Approaches". This collection features selected peer-reviewed contributions that showcase the vibrant and diverse research presented at meeting. This book is intended for a broad audience interested in statistics and aims at providing stimulating contributions to theoretical, methodological, and computational aspects of Bayesian statistics. The contributions highlight various topics in Bayesian statistics, presenting promising methodological approaches to address critical challenges across diverse applications. This compilation stands as a testament to the talent and potential within the j-ISBA community. This book is meant to serve as a catalyst for continued advancements in Bayesian methodology and its applications and encourages fruitful collaborations that push the boundaries of statistical research.
Author | : Xuewen Yu |
Publisher | : |
Total Pages | : 0 |
Release | : 2022 |
Genre | : Bayesian statistical decision theory |
ISBN | : |
Author | : Rodrigo A. Collazo |
Publisher | : |
Total Pages | : 500 |
Release | : 2017 |
Genre | : Bayesian statistical decision theory |
ISBN | : |
Author | : Raffaele Argiento |
Publisher | : Springer Nature |
Total Pages | : 122 |
Release | : 2022-11-26 |
Genre | : Mathematics |
ISBN | : 303116427X |
This book presents a selection of peer-reviewed contributions to the fifth Bayesian Young Statisticians Meeting, BaYSM 2021, held virtually due to the COVID-19 pandemic on 1-3 September 2021. Despite all the challenges of an online conference, the meeting provided a valuable opportunity for early career researchers, including MSc students, PhD students, and postdocs to connect with the broader Bayesian community. The proceedings highlight many different topics in Bayesian statistics, presenting promising methodological approaches to address important challenges in a variety of applications. The book is intended for a broad audience of people interested in statistics, and provides a series of stimulating contributions on theoretical, methodological, and computational aspects of Bayesian statistics.
Author | : Marcus Bendtsen |
Publisher | : Linköping University Electronic Press |
Total Pages | : 245 |
Release | : 2017-06-08 |
Genre | : |
ISBN | : 9176855252 |
Bayesian networks have grown to become a dominant type of model within the domain of probabilistic graphical models. Not only do they empower users with a graphical means for describing the relationships among random variables, but they also allow for (potentially) fewer parameters to estimate, and enable more efficient inference. The random variables and the relationships among them decide the structure of the directed acyclic graph that represents the Bayesian network. It is the stasis over time of these two components that we question in this thesis. By introducing a new type of probabilistic graphical model, which we call gated Bayesian networks, we allow for the variables that we include in our model, and the relationships among them, to change overtime. We introduce algorithms that can learn gated Bayesian networks that use different variables at different times, required due to the process which we are modelling going through distinct phases. We evaluate the efficacy of these algorithms within the domain of algorithmic trading, showing how the learnt gated Bayesian networks can improve upon a passive approach to trading. We also introduce algorithms that detect changes in the relationships among the random variables, allowing us to create a model that consists of several Bayesian networks, thereby revealing changes and the structure by which these changes occur. The resulting models can be used to detect the currently most appropriate Bayesian network, and we show their use in real-world examples from both the domain of sports analytics and finance.