Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Markov Chains and Stochastic Stability
New up-to-date edition of this influential classic on Markov chains in general state spaces. Proofs are rigorous and concise, the range of applications is broad and knowledgeable, and key ideas are accessible to practitioners with limited mathematical background. New commentary by Sean Meyn, including updated references, reflects developments since 1996.
Attractors for Semigroups and Evolution Equations
In this volume, Olga A. Ladyzhenskaya expands on her highly successful 1991 Accademia Nazionale dei Lincei lectures. The lectures were devoted to questions of the behaviour of trajectories for semigroups of nonlinear bounded continuous operators in a locally non-compact metric space and for solutions of abstract evolution equations. The latter contain many initial boundary value problems for dissipative partial differential equations. This work, for which Ladyzhenskaya was awarded the Russian Academy of Sciences' Kovalevskaya Prize, reflects the high calibre of her lectures; it is essential reading for anyone interested in her approach to partial differential equations and dynamical systems. This edition, reissued for her centenary, includes a new technical introduction, written by Gregory A. Seregin, Varga K. Kalantarov and Sergey V. Zelik, surveying Ladyzhenskaya's works in the field and subsequent developments influenced by her results.
Markov Chains and Stochastic Stability
Markov Chains and Stochastic Stability is part of the Communications and Control Engineering Series (CCES) edited by Professors B.W. Dickinson, E.D. Sontag, M. Thoma, A. Fettweis, J.L. Massey and J.W. Modestino. The area of Markov chain theory and application has matured over the past 20 years into something more accessible and complete. It is of increasing interest and importance. This publication deals with the action of Markov chains on general state spaces. It discusses the theories and the use to be gained, concentrating on the areas of engineering, operations research and control theory. Throughout, the theme of stochastic stability and the search for practical methods of verifying suc...
Distributional Reinforcement Learning
The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective. Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. Going beyond the common approach to reinforcement learning and expected values, it focuses on the total reward or return obtained as a consequence of an agent's choices—specifically, how this return behaves from a probabilistic perspective. In this first comprehensive guide to distributional reinforcement learning, Marc G. Bellemare, Will Dabney, and Mark Rowland, who spearheaded development of the field, present its key...
An Introduction to Symbolic Dynamics and Coding
Elementary introduction to symbolic dynamics, updated to describe the main advances in the subject since the original publication in 1995.
Effects of Problem-Oriented Policing on Crime and Disorder
This is a print on demand edition of a hard to find publication. Problem-Oriented Policing (POP) approach was one response to a crisis in policing that emerged in the 1970s and 1980s. Police were not being effective in preventing crime because they had become focused on the ¿means¿ of policing and had neglected the ¿goals¿ of preventing and controlling crime. The ¿problem¿ rather than calls or crime incidents should be the focus. This study conducted a review to examine the effectiveness of POP in reducing crime and disorder. Studies had to meet 3 criteria: (1) the SARA model was used; (2) a comparison group was included; (3) at least one crime or disorder outcome was reported. Only 10 studies that met the criteria; there was a modest but statistically significant impact of POP on crime.
Fourier Analysis
A lively and engaging look at some of the ideas, techniques and elegant results of Fourier analysis, and their applications.
Meta-Analysis in Medicine and Health Policy
This remarkable text raises the analysis of data in health sciences and policy to new heights of refinement and applicability by introducing cutting-edge meta-analysis strategies while reviewing more commonly used techniques. Each chapter builds on sound principles, develops methodologies to solve statistical problems, and presents concrete applications used by experienced medical practitioners and health policymakers. Written by more than 30 celebrated international experts, Meta-Analysis in Medicine and Health Policy employs copious examples and pictorial presentations to teach and reinforce biostatistical techniques more effectively and poses numerous open questions of medical and health policy research.
Transcendental Number Theory
Alan Baker's systematic account of transcendental number theory, with a new introduction and afterword explaining recent developments.
Stochastic Methods for Parameter Estimation and Design of Experiments in Systems Biology
Markov Chain Monte Carlo (MCMC) methods are sampling based techniques, which use random numbers to approximate deterministic but unknown values. They can be used to obtain expected values, estimate parameters or to simply inspect the properties of a non-standard, high dimensional probability distribution. Bayesian analysis of model parameters provides the mathematical foundation for parameter estimation using such probabilistic sampling. The strengths of these stochastic methods are their robustness and relative simplicity even for nonlinear problems with dozens of parameters as well as a built-in uncertainty analysis. Because Bayesian model analysis necessarily involves the notion of prior ...
