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"Statistical Modeling of Occupant Behavior" is a comprehensive guide that explores the statistical analysis, modeling, and prediction of how occupants interact with building components and systems, impacting energy consumption and indoor conditions. The book, authored by researchers from Denmark, Germany, Canada, and Switzerland, covers various statistical models and distributions, including linear and generalized linear models, mixed effects models, hierarchical models, survival analysis, time-series models, Markov chain models, state space models, and hidden Markov models. Each chapter includes examples and R code for practical application. This resource is valuable for researchers, practitioners, instructors, and graduate students in a range of fields and applications. [Extracted from the article]

While this text introduces readers to statistical modeling approaches for infectious diseases and provides important opportunities for practice and implementation in the way of exercises and example code, it also falls short in several areas. Keywords: Bayesian inference; compartmental models; stochastic; WINBUGS EN Bayesian inference compartmental models stochastic WINBUGS 412 413 2 04/04/22 20220301 NES 220301 Compartmental infectious disease modeling has been dominated historically by deterministic and stochastic differential equations-based methods, and available textbooks on the subject reflect this history. Given the established history of differential equations-based infectious disease modeling, the choice to focus on stochastic differential equations-based models and associated Bayesian inference in this text is natural. [Extracted from the article]

Both biologists and new mechanists who take a more ontologically extravagant view of mechanisms are targets of Baetu's critiques. Biologists might have preferred more discussion of the discovery strategies that Baetu says are part of the new mechanists' epistemic project rather than his critiques of audacious claims in their metaphysical projects. Since the 1990s, philosophers of biology have analyzed biologists' use of mechanisms, rather than mathematical laws of physics, to explain biological phenomena. [Extracted from the article]

Strengths of this text include its substantial coverage of basic probability, abundance of student exercises, and inclusion of many practically relevant examples and exercises. Dr. Ghahramani has also added "...538 new examples and exercises to this edition, almost all of which are of applied nature and in realistic contexts." In short, I Fundamentals of Probability with Stochastic Processes i would make an excellent text book for a one-semester (-quarter) course on basic probability I and i a follow-up course on more advanced probability or stochastic processes. [Extracted from the article]

The article reviews the book "Large Deviations for Stochastic Processes," by Jin Feng and Thomas G. Kurtz.

The article reviews the book "Fundamentals of Stochastic Filtering," by Alan Bain and Dan Crisan.

The article reviews the book "Theory and Applications of Stochastic Processes: An Analytical Approach," by Zeev Schuss.

The article reviews the book "Point Process Theory and Applications: Marked Point and Piecewise Deterministic Processes," by Martin Jacobsen.

The shift to multi-stage detection leads to the formulation of a new detection problem, known as Variant E. Chapter 7 also connects disorder detection to classical results on hypothesis testing by Wald and by Neyman and Pearson. Chapter 8 reformulates the I G i -distribution disorder detection problem on filtered probability spaces. To conclude, "Stochastic Disorder Problems" is an ideal textbook for researchers in probability theory and mathematical statistics interested in quickest detection methods, optimal stopping, and sequential analysis. [Extracted from the article]

He explains that these are objects that can be described using "random variables, vectors or matrices, stochastic processes, integrals and differential equations, or point processes and random sets" (p. xv). Although this target group typically has some prior knowledge of probability theory, Chapter 1 of the book is a nonrigorous introduction to elementary probability theory under the title "rudimentary models." With regard to precision, the "rudimentary models" in the first chapter lack accuracy, and slight mathematical inconsistencies appear at several places in the book (e.g., stochastic processes are introduced with general index set I T i , but properties like independent increments are formulated for I T i = [0, I i ) without specifying this). [Extracted from the article]

Bayesian inference is a powerful method for incorporating prior information in estimation, hypothesis testing, and prediction of future events. Chapter 1 introduces Bayesian inference for stochastic processes, and gives a summary of the book. Readers familiar with Bayesian inference and probability theory can jump straight to chapter 4, which introduces Bayesian inference for discrete Markov chains. [Extracted from the article]

The continuous-time processes considered range from basic Poisson processes all the way to nonlinear jump diffusions and mean field particle models. There is also a "stochastic analysis toolbox" designed to give the reader an (initial) idea about analytic and probabilistic techniques such as Poincaré inequalities, Lyapunov techniques, or time changes. Part five of the book covers stochastic processes on manifolds and, spanning some 150 pages, is almost a small book in its own right. [Extracted from the article]

Reviews the book 'Stochasticism: Cross-Cultural and Comparative Perspectives,' edited by José Ignacio Cabézon.

This article presents review information of two books "Discrete Stochastics," by Konrad Jacobs and "Adventures in Stochastic Processes," by Sidney Resnick, with providing a bibliography of related books. These books reportedly deal with basic notions and methods of probability theory and the theory of stochastic processes. The presentation of the material in these textbooks is based on quite different principles reflecting two approaches that currently exist on teaching probability theory. The book by Jacobs presents a descriptive illustration of "discrete" probability theory, whereas the book by Resnick hints at the possibility of a relatively easy study of the theory of stochastic processes.

Reviews some recent probabilistic choice models for paired comparisons data, explaining the differences between various forms of stochastic transitivity. Moderate utility model; Three recent families of pairwise choice models; Wandering vector and wandering ideal point models; Stochastic tree unfolding models; Illustrative applications; Conclusion.

Reviews the book "Stochastic Economics. Stochastic Processes, Control, and Programming," by Gerhard Tintner and Jati K. Sengupta.

Reviews the book 'A Million Random Digits With 100,000 Normal Deviates.'

This textbook starts with an introduction (Chapter 1) and then splits into two main parts: hypothesis testing and estimation. In Chapters 12 and 13, classical estimation methods are discussed, with results on unbiased minimum-variance estimation, sufficient statistics, completeness and the Cram I é i r-Rao lower bound on the variance. The book presents a rigorous and unified coverage of fundamental concepts and results for statistical inference and especially well-selected topics and applications in engineering and data science. [Extracted from the article]

Highlights from the article: Each chapter covers a large number of topics on Monte Carlo simulation along with computer algorithms and a good number of exercises. Chapter 1 covers some introductory probability and statistics needed for subsequent chapters. Two alternative approaches called the variance minimization and the cross-entropy methods along with importance sampling algorithms in which paths are generated in a sequential manner are discussed in this chapter.

Reviews the book 'Stochastic Modelling and Analysis: A Computational Approach,' by Henk C. Tijms.

The article reviews the book "Stochastische Integration und Zeitreihenmodellierung," by Uwe Hassler.

The article reviews the book "Stochastic Processes With Application to Finance," by Masaaki Kijima.

The article reviews the book "Quantum Stochastic Processes and Noncommutative Geometry," by Kalyan B. Sinha and Debashish Goswami.

The article reviews the book "Louis Bachelier: Aux origines de la finance mathématique," edited by Jean-Michel Courtault and Your Kabanov.

The article reviews the book "Stochastic Processes and Models" by David Stirzaker.

Reviews the book "Stochastic Frontier Analysis," by Subal Kumbhakar and C. A. Knox Lovell.

Reviews the book "Fundamentals of Queueing Networks: Performance, Asymptotics, and Optimization," by H. Chen and D. D. Yao.

Reviews the book 'Stochastic in Finite and Infinite Dimensions. In Honor of Gopinath Kallianpur,' edited by T. Hida, R.L. Karandikar, H. Kunita, B.S. Rajput, S. Watanabe and J. Xiong.

Reviews the book "Stochastic Optimization: Algorithms and Applications," by Stanislav Uryasev and Panos M. Pardalos.

Reviews the book 'Stochastic Methods in Structural Dynamics,' edited by G. Schueler and M. Shinozuka.

Reviews the book 'Stochastic Processes in Physics and Chemistry,' by N.G. van Kampen.

Reviews the book "Urban Systems: Contemporary Approaches to Modelling," edited by C. S. Bertuglia, G. Leonardi, S. Occelli, G. A. Rabino, R. Tadei, and A. G. Wilson.

Reviews the book "Estimation, Inference and Specification Analysis," by H. White.

Reviews the book "Numerical Techniques for Stochastic Optimization," edited by Y. Ermoliev and R. Wets.

Reviews the book "Stochastische Optimierung bei partieller Information," by P. Abel.

Reviews the book "Stochastic Models for Social Processes," by David J. Bartholomew.