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Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain Monte-Carlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a self-contained introduction to Fourier and discrete Fourier methods. There is a chapter devoted to Bayesian inference with Poisson sampling, and three chapters on frequentist methods help to bridge the gap between the frequentist and Bayesian approaches. Supporting Mathematica® notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at www.cambridge.org/9780521150125.

Bayesian statistical decision theory. --- Physical sciences --- Statistische data-analyse. --- Statistical methods. --- Mathematica (Computer file). --- Science --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Mathematica (Computer file)

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Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies.

Bioinformatics --- Medical informatics --- Bayesian statistical decision theory. --- Methodology. --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Clinical informatics --- Health informatics --- Medical information science --- Information science --- Medicine --- Bio-informatics --- Biological informatics --- Biology --- Computational biology --- Systems biology --- Data processing --- Bayesian statistical decision theory --- Methodology

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Markov Chain Monte Carlo (MCMC) originated in statistical physics, but has spilled over into various application areas, leading to a corresponding variety of techniques and methods. That variety stimulates new ideas and developments from many different places, and there is much to be gained from cross-fertilization. This book presents five expository essays by leaders in the field, drawing from perspectives in physics, statistics and genetics, and showing how different aspects of MCMC come to the fore in different contexts. The essays derive from tutorial lectures at an interdisciplinary progr

Monte Carlo method --- Bayesian statistical decision theory --- Markov processes --- Monte-Carlo, Méthode de --- Statistique bayésienne --- Markov, Processus de --- Monte Carlo method. --- Bayesian statistical decision theory. --- Markov processes. --- Simulatiemodellen. --- Analysis, Markov --- Chains, Markov --- Markoff processes --- Markov analysis --- Markov chains --- Markov models --- Models, Markov --- Processes, Markov --- Stochastic processes --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Artificial sampling --- Model sampling --- Monte Carlo simulation --- Monte Carlo simulation method --- Stochastic sampling --- Games of chance (Mathematics) --- Mathematical models --- Numerical analysis --- Numerical calculations --- Markov-processen. --- Monte Carlo-methode.