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Formal Methods in Systems Biology ; 1st International Workshop, FMSB 2008, Cambridge, UK, June 4-5, 2008. Proceedings
This book constitutes the refereed proceedings of the First International Workshop on Formal Methods in Systems Biology, FMSB 2008, held in Cambridge, UK, in June, 2008.The 9 revised full papers presented were carefully reviewed and selected from the workshop lectures that all were invited contributions. The purpose of this meeting was to identify techniques for the specification, development and verification of biological models. It also focused on the design of tools to execute and analyze biological models that can significantly advance our understanding of biological systems.
Formal Methods for Computational Systems Biology ; 8th International School on Formal Methods for the Design of Computer, Communication, and Software Systems, SFM 2008 Bertinoro, Italy, June 2-7, 2008 Advanced Lectures
This volume presents the set of papers accompanying the lectures of the eighth International School on Formal Methods for the Design of Computer, Com- nication, and Software Systems (SFM). This series of schools addresses the use of formal methods in computer science asaprominent approach to theri gorousdesign of computer, communication, and software systems. The main aim of the SFM series is to ofer a good spectrum of current research in foundations as well as applications of formal methods, which can be of help for graduate students and young researchers who intend to approach the feld.
Bayesian reliability
Bayesian Reliability presents modern methods and techniques for analyzing reliability data from a Bayesian perspective. The adoption and application of Bayesian methods in virtually all branches of science and engineering have significantly increased over the past few decades. This increase is largely due to advances in simulation-based computational tools for implementing Bayesian methods. The authors extensively use such tools throughout this book, focusing on assessing the reliability of components and systems with particular attention to hierarchical models and models incorporating explanatory variables. Such models include failure time regression models, accelerated testing models, and degradation models. The authors pay special attention to Bayesian goodness-of-fit testing, model validation, reliability test design, and assurance test planning. Throughout the book, the authors use Markov chain Monte Carlo (MCMC) algorithms for implementing Bayesian analyses--algorithms that make the Bayesian approach to reliability computationally feasible and conceptually straightforward.
Bayesian computation with R : Introduces Bayesian modeling by use of computation using the R language
R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry. Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language.
