Time Series Analysis For The State Space Model With R Stan
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Time Series Analysis For The State Space Model With R Stan
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Author : Junichiro Hagiwara
language : en
Publisher: Springer Nature
Release Date : 2021-08-30
Time Series Analysis For The State Space Model With R Stan written by Junichiro Hagiwara and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-30 with Mathematics categories.
This book provides a comprehensive and concrete illustration of time series analysis focusing on the state-space model, which has recently attracted increasing attention in a broad range of fields. The major feature of the book lies in its consistent Bayesian treatment regarding whole combinations of batch and sequential solutions for linear Gaussian and general state-space models: MCMC and Kalman/particle filter. The reader is given insight on flexible modeling in modern time series analysis. The main topics of the book deal with the state-space model, covering extensively, from introductory and exploratory methods to the latest advanced topics such as real-time structural change detection. Additionally, a practical exercise using R/Stan based on real data promotes understanding and enhances the reader’s analytical capability.
Time Series Analysis For The State Space Model With R Stan
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Author : Junichiro Hagiwara
language : en
Publisher:
Release Date : 2021
Time Series Analysis For The State Space Model With R Stan written by Junichiro Hagiwara and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
This book provides a comprehensive and concrete illustration of time series analysis focusing on the state-space model, which has recently attracted increasing attention in a broad range of fields. The major feature of the book lies in its consistent Bayesian treatment regarding whole combinations of batch and sequential solutions for linear Gaussian and general state-space models: MCMC and Kalman/particle filter. The reader is given insight on flexible modeling in modern time series analysis. The main topics of the book deal with the state-space model, covering extensively, from introductory and exploratory methods to the latest advanced topics such as real-time structural change detection. Additionally, a practical exercise using R/Stan based on real data promotes understanding and enhances the reader's analytical capability. .
Bayesian Statistical Modeling With Stan R And Python
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Author : Kentaro Matsuura
language : en
Publisher: Springer Nature
Release Date : 2023-01-24
Bayesian Statistical Modeling With Stan R And Python written by Kentaro Matsuura and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-24 with Computers categories.
This book provides a highly practical introduction to Bayesian statistical modeling with Stan, which has become the most popular probabilistic programming language. The book is divided into four parts. The first part reviews the theoretical background of modeling and Bayesian inference and presents a modeling workflow that makes modeling more engineering than art. The second part discusses the use of Stan, CmdStanR, and CmdStanPy from the very beginning to basic regression analyses. The third part then introduces a number of probability distributions, nonlinear models, and hierarchical (multilevel) models, which are essential to mastering statistical modeling. It also describes a wide range of frequently used modeling techniques, such as censoring, outliers, missing data, speed-up, and parameter constraints, and discusses how to lead convergence of MCMC. Lastly, the fourth part examines advanced topics for real-world data: longitudinal data analysis, state space models, spatial data analysis, Gaussian processes, Bayesian optimization, dimensionality reduction, model selection, and information criteria, demonstrating that Stan can solve any one of these problems in as little as 30 lines. Using numerous easy-to-understand examples, the book explains key concepts, which continue to be useful when using future versions of Stan and when using other statistical modeling tools. The examples do not require domain knowledge and can be generalized to many fields. The book presents full explanations of code and math formulas, enabling readers to extend models for their own problems. All the code and data are on GitHub.
Natural Geo Disasters And Resiliency
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Author : Hemanta Hazarika
language : en
Publisher: Springer Nature
Release Date : 2024-05-03
Natural Geo Disasters And Resiliency written by Hemanta Hazarika and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-03 with Technology & Engineering categories.
This book presents select proceedings of the 2nd International Conference on Construction Resources for Environmentally Sustainable Technologies (CREST 2023), and focuses on sustainability, promotion of new ideas and innovations in design, construction and maintenance of geotechnical structures with the aim of contributing towards climate change adaptation and disaster resiliency to meet the UN Sustainable Development Goals (SDGs). It presents latest research, information, technological advancement, practical challenges encountered, and solutions adopted in the field of geotechnical engineering for sustainable infrastructure towards climate change adaptation. This volume will be of interest to those in academia and industry alike.
Ethics In Statistics
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Author : Hassan Doosti
language : en
Publisher: Ethics International Press
Release Date : 2024-03-29
Ethics In Statistics written by Hassan Doosti and has been published by Ethics International Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-29 with Reference categories.
Data plays a vital role in different parts of our lives. In the world of big data, and policy determined by a variety of statistical artifacts, discussions around the ethics of data gathering, manipulation and presentation are increasingly important. Ethics in Statistics aims to make a significant contribution to that debate. The processes of gathering data through sampling, summarising of the findings, and extending results to a population, need to be checked via an ethical prospective, as well as a statistical one. Statistical learning without ethics can be harmful for mankind. This edited collection brings together contributors in the field of data science, data analytics and statistics, to share their thoughts about the role of ethics in different aspects of statistical learning.
The Handbook Of Personality Dynamics And Processes
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Author : John F. Rauthmann
language : en
Publisher: Academic Press
Release Date : 2021-01-20
The Handbook Of Personality Dynamics And Processes written by John F. Rauthmann and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-20 with Psychology categories.
The Handbook of Personality Dynamics and Processes is a primer to the basic and most important concepts, theories, methods, empirical findings, and applications of personality dynamics and processes. This book details how personality psychology has evolved from descriptive research to a more explanatory and dynamic science of personality, thus bridging structure- and process-based approaches, and it also reflects personality psychology's interest in the dynamic organization and interplay of thoughts, feelings, desires, and actions within persons who are always embedded into social, cultural and historic contexts. The Handbook of Personality Dynamics and Processes tackles each topic with a range of methods geared towards assessing and analyzing their dynamic nature, such as ecological momentary sampling of personality manifestations in real-life; dynamic modeling of time-series or longitudinal personality data; network modeling and simulation; and systems-theoretical models of dynamic processes. - Ties topics and methods together for a more dynamic understanding of personality - Summarizes existing knowledge and insights of personality dynamics and processes - Covers a broad compilation of cutting-edge insights - Addresses the biophysiological and social mechanisms underlying the expression and effects of personality - Examines within-person consistency and variability
Dynamic Time Series Models Using R Inla
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Author : Nalini Ravishanker
language : en
Publisher: CRC Press
Release Date : 2022-08-10
Dynamic Time Series Models Using R Inla written by Nalini Ravishanker and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-10 with Mathematics categories.
Dynamic Time Series Models using R-INLA: An Applied Perspective is the outcome of a joint effort to systematically describe the use of R-INLA for analysing time series and showcasing the code and description by several examples. This book introduces the underpinnings of R-INLA and the tools needed for modelling different types of time series using an approximate Bayesian framework. The book is an ideal reference for statisticians and scientists who work with time series data. It provides an excellent resource for teaching a course on Bayesian analysis using state space models for time series. Key Features: Introduction and overview of R-INLA for time series analysis. Gaussian and non-Gaussian state space models for time series. State space models for time series with exogenous predictors. Hierarchical models for a potentially large set of time series. Dynamic modelling of stochastic volatility and spatio-temporal dependence.
Accelerating Monte Carlo Methods For Bayesian Inference In Dynamical Models
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Author : Johan Dahlin
language : en
Publisher: Linköping University Electronic Press
Release Date : 2016-03-22
Accelerating Monte Carlo Methods For Bayesian Inference In Dynamical Models written by Johan Dahlin and has been published by Linköping University Electronic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-22 with categories.
Making decisions and predictions from noisy observations are two important and challenging problems in many areas of society. Some examples of applications are recommendation systems for online shopping and streaming services, connecting genes with certain diseases and modelling climate change. In this thesis, we make use of Bayesian statistics to construct probabilistic models given prior information and historical data, which can be used for decision support and predictions. The main obstacle with this approach is that it often results in mathematical problems lacking analytical solutions. To cope with this, we make use of statistical simulation algorithms known as Monte Carlo methods to approximate the intractable solution. These methods enjoy well-understood statistical properties but are often computational prohibitive to employ. The main contribution of this thesis is the exploration of different strategies for accelerating inference methods based on sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC). That is, strategies for reducing the computational effort while keeping or improving the accuracy. A major part of the thesis is devoted to proposing such strategies for the MCMC method known as the particle Metropolis-Hastings (PMH) algorithm. We investigate two strategies: (i) introducing estimates of the gradient and Hessian of the target to better tailor the algorithm to the problem and (ii) introducing a positive correlation between the point-wise estimates of the target. Furthermore, we propose an algorithm based on the combination of SMC and Gaussian process optimisation, which can provide reasonable estimates of the posterior but with a significant decrease in computational effort compared with PMH. Moreover, we explore the use of sparseness priors for approximate inference in over-parametrised mixed effects models and autoregressive processes. This can potentially be a practical strategy for inference in the big data era. Finally, we propose a general method for increasing the accuracy of the parameter estimates in non-linear state space models by applying a designed input signal. Borde Riksbanken höja eller sänka reporäntan vid sitt nästa möte för att nå inflationsmålet? Vilka gener är förknippade med en viss sjukdom? Hur kan Netflix och Spotify veta vilka filmer och vilken musik som jag vill lyssna på härnäst? Dessa tre problem är exempel på frågor där statistiska modeller kan vara användbara för att ge hjälp och underlag för beslut. Statistiska modeller kombinerar teoretisk kunskap om exempelvis det svenska ekonomiska systemet med historisk data för att ge prognoser av framtida skeenden. Dessa prognoser kan sedan användas för att utvärdera exempelvis vad som skulle hända med inflationen i Sverige om arbetslösheten sjunker eller hur värdet på mitt pensionssparande förändras när Stockholmsbörsen rasar. Tillämpningar som dessa och många andra gör statistiska modeller viktiga för många delar av samhället. Ett sätt att ta fram statistiska modeller bygger på att kontinuerligt uppdatera en modell allteftersom mer information samlas in. Detta angreppssätt kallas för Bayesiansk statistik och är särskilt användbart när man sedan tidigare har bra insikter i modellen eller tillgång till endast lite historisk data för att bygga modellen. En nackdel med Bayesiansk statistik är att de beräkningar som krävs för att uppdatera modellen med den nya informationen ofta är mycket komplicerade. I sådana situationer kan man istället simulera utfallet från miljontals varianter av modellen och sedan jämföra dessa mot de historiska observationerna som finns till hands. Man kan sedan medelvärdesbilda över de varianter som gav bäst resultat för att på så sätt ta fram en slutlig modell. Det kan därför ibland ta dagar eller veckor för att ta fram en modell. Problemet blir särskilt stort när man använder mer avancerade modeller som skulle kunna ge bättre prognoser men som tar för lång tid för att bygga. I denna avhandling använder vi ett antal olika strategier för att underlätta eller förbättra dessa simuleringar. Vi föreslår exempelvis att ta hänsyn till fler insikter om systemet och därmed minska antalet varianter av modellen som behöver undersökas. Vi kan således redan utesluta vissa modeller eftersom vi har en bra uppfattning om ungefär hur en bra modell ska se ut. Vi kan också förändra simuleringen så att den enklare rör sig mellan olika typer av modeller. På detta sätt utforskas rymden av alla möjliga modeller på ett mer effektivt sätt. Vi föreslår ett antal olika kombinationer och förändringar av befintliga metoder för att snabba upp anpassningen av modellen till observationerna. Vi visar att beräkningstiden i vissa fall kan minska ifrån några dagar till någon timme. Förhoppningsvis kommer detta i framtiden leda till att man i praktiken kan använda mer avancerade modeller som i sin tur resulterar i bättre prognoser och beslut.
Collective Political Rationality
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Author : Gregory E. McAvoy
language : en
Publisher: Routledge
Release Date : 2015-05-15
Collective Political Rationality written by Gregory E. McAvoy and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-15 with Political Science categories.
Amidst the polarization of contemporary politics, partisan loyalties among citizens are regarded as one contributor to political stalemate. Partisan loyalties lead Democrats and Republicans to look at the same economic information but to come to strikingly different conclusions about the state of the economy and the performance of the president in managing it. As a result, many observers argue that democratic politics would work better if citizens would shed their party loyalty and more dispassionately assess political and economic news. In this book, Gregory E. McAvoy argues—contra this conventional wisdom; that partisanship is a necessary feature of modern politics, making it feasible for citizens to make some sense of the vast number of issues that make their way onto the political agenda. Using unique data, he shows that the biases and distortions that partisanship introduces to collective opinion are real, but despite them, collective opinion changes meaningfully in response to economic and political news. In a comparison of the public’s assessment of the economy to those of economic experts, he finds a close correspondence between the two over time, and that in modern democracies an informed public will also necessarily be partisan. Modernizing the study of collective opinion, McAvoy's book is essential reading for scholars of American Public Opinion and Political Behavior.
Bayesian Hierarchical Models
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Author : Peter D. Congdon
language : en
Publisher: CRC Press
Release Date : 2019-09-16
Bayesian Hierarchical Models written by Peter D. Congdon and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-16 with Mathematics categories.
An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections. Through illustrative data analysis and attention to statistical computing, this book facilitates practical implementation of Bayesian hierarchical methods. The new edition is a revision of the book Applied Bayesian Hierarchical Methods. It maintains a focus on applied modelling and data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. This latter chapter is particularly important, due to recent advances in Bayesian computing and estimation, including the development of rjags and rstan. It also features updates throughout with new examples. The examples exploit and illustrate the broader advantages of the R computing environment, while allowing readers to explore alternative likelihood assumptions, regression structures, and assumptions on prior densities. Features: Provides a comprehensive and accessible overview of applied Bayesian hierarchical modelling Includes many real data examples to illustrate different modelling topics R code (based on rjags, jagsUI, R2OpenBUGS, and rstan) is integrated into the book, emphasizing implementation Software options and coding principles are introduced in new chapter on computing Programs and data sets available on the book’s website