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Decision Making, Computer-Assisted --- Models, Statistical --- Bayes Theorem --- Decision Support Techniques --- Medicine --- Bayesian statistical decision theory

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The term probability can be used in two main senses. In the frequency interpretation it is a limiting ratio in a sequence of repeatable events. In the Bayesian view, probability is a mental construct representing uncertainty. This 2002 book is about these two types of probability and investigates how, despite being adopted by scientists and statisticians in the eighteenth and nineteenth centuries, Bayesianism was discredited as a theory of scientific inference during the 1920s and 1930s. Through the examination of a dispute between two British scientists, the author argues that a choice between the two interpretations is not forced by pure logic or the mathematics of the situation, but depends on the experiences and aims of the individuals involved. The book should be of interest to students and scientists interested in statistics and probability theories and to general readers with an interest in the history, sociology and philosophy of science.

Probabilities. --- Bayesian statistical decision theory. --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Probability --- Statistical inference --- Combinations --- Mathematics --- Chance --- Least squares --- Mathematical statistics --- Risk --- Jeffreys, Harold, --- Fisher, Ronald Aylmer, --- Fisher, R. A. --- Fisher, Ronald A. --- Dzheffris, Garolʹd, --- Bayesian statistical decision theory --- Probabilities --- Arts and Humanities --- Philosophy

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Bayesian statistical decision theory --- Social sciences --- #SBIB:303H520 --- 303 --- 519.226 --- 303 Methoden bij sociaalwetenschappelijk onderzoek --- Methoden bij sociaalwetenschappelijk onderzoek --- 519.226 Inference and decision theory. Likelihood. Bayesian theory. Fiducial probability --- Inference and decision theory. Likelihood. Bayesian theory. Fiducial probability --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Statistical methods --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Mathematical statistics

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This 2002 book investigates the opportunities in building intelligent decision support systems offered by multi-agent distributed probabilistic reasoning. Probabilistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become increasingly an active field of research and practice in artificial intelligence, operations research and statistics. The success of this technique in modeling intelligent decision support systems under the centralized and single-agent paradigm has been striking. Yang Xiang extends graphical dependence models to the distributed and multi-agent paradigm. He identifies the major technical challenges involved in such an endeavor and presents the results. The framework developed in the book allows distributed representation of uncertain knowledge on a large and complex environment embedded in multiple cooperative agents, and effective, exact and distributed probabilistic inference.

Distributed artificial intelligence --- Bayesian statistical decision theory --- Intelligent agents (Computer software) --- Intelligence artificielle répartie --- Statistique bayésienne --- Agents intelligents (logiciels) --- Data processing --- Informatique --- Information Technology --- Computer Science (Hardware & Networks) --- Distributed artificial intelligence. --- Intelligent agents (Computer software). --- Computer Science --- Engineering & Applied Sciences --- Data processing. --- Intelligence artificielle répartie --- Statistique bayésienne --- Agents, Autonomous (Computer software) --- Agents, Cognitive (Computer software) --- Agents, Intelligent (Computer software) --- Assistants, Cognitive (Computer software) --- Assistants, Intelligent software --- Autonomous agents (Computer software) --- Cognitive agents (Computer software) --- Cognitive assistants (Computer software) --- IAs (Computer software) --- Intelligent agent software --- Intelligent software agents --- Intelligent software assistants --- Software agents (Computer software) --- Special agents (Computer software) --- Bayes' solution --- Bayesian analysis --- DAI (Artificial intelligence) --- Distributed AI (Artificial intelligence) --- Artificial intelligence --- Statistical decision --- Computer programs --- E-books --- Multiagent systems. --- Agent-based model (Computer software) --- MASs (Multiagent systems) --- Multi-agent systems --- Systems, Multiagent

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Over the last ten years the introduction of computer intensive statistical methods has opened new horizons concerning the probability models that can be fitted to genetic data, the scale of the problems that can be tackled and the nature of the questions that can be posed. In particular, the application of Bayesian and likelihood methods to statistical genetics has been facilitated enormously by these methods. Techniques generally referred to as Markov chain Monte Carlo (MCMC) have played a major role in this process, stimulating synergies among scientists in different fields, such as mathematicians, probabilists, statisticians, computer scientists and statistical geneticists. Specifically, the MCMC "revolution" has made a deep impact in quantitative genetics. This can be seen, for example, in the vast number of papers dealing with complex hierarchical models and models for detection of genes affecting quantitative or meristic traits in plants, animals and humans that have been published recently. This book, suitable for numerate biologists and for applied statisticians, provides the foundations of likelihood, Bayesian and MCMC methods in the context of genetic analysis of quantitative traits. Most students in biology and agriculture lack the formal background needed to learn these modern biometrical techniques. Although a number of excellent texts in these areas have become available in recent years, the basic ideas and tools are typically described in a technically demanding style, and have been written by and addressed to professional statisticians. For this reason, considerable more detail is offered than what may be warranted for a more mathematically apt audience. The book is divided into four parts. Part I gives a review of probability and distribution theory. Parts II and III present methods of inference and MCMC methods. Part IV discusses several models that can be applied in quantitative genetics, primarily from a bayesian perspective. An effort has been made to relate biological to statistical parameters throughout, and examples are used profusely to motivate the developments.

519.226 --- 57.087.1 --- 575 --- Genetics --- -Monte Carlo method --- Markov processes --- Bayesian statistical decision theory. --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Analysis, Markov --- Chains, Markov --- Markoff processes --- Markov analysis --- Markov chains --- Markov models --- Models, Markov --- Processes, Markov --- Stochastic processes --- Artificial sampling --- Model sampling --- Monte Carlo simulation --- Monte Carlo simulation method --- Stochastic sampling --- Games of chance (Mathematics) --- Mathematical models --- Numerical analysis --- Numerical calculations --- Biology --- Embryology --- Mendel's law --- Adaptation (Biology) --- Breeding --- Chromosomes --- Heredity --- Mutation (Biology) --- Variation (Biology) --- 575 General genetics. General cytogenetics. Immunogenetics. Evolution. Speciation. Phylogeny --- General genetics. General cytogenetics. Immunogenetics. Evolution. Speciation. Phylogeny --- 57.087.1 Biometry. Statistical study and treatment of biological data --- Biometry. Statistical study and treatment of biological data --- 519.226 Inference and decision theory. Likelihood. Bayesian theory. Fiducial probability --- Inference and decision theory. Likelihood. Bayesian theory. Fiducial probability --- Statistical methods --- Génétique quantitative --- Genetics -- Statistical methods. --- Monte Carlo Method --- Genetics, Medical --- Systems Analysis --- Basic Sciences. Genetics --- Population and Quantitative Genetics --- Population and Quantitative Genetics. --- Génétique --- Statistique bayésienne --- Life sciences. --- Biochemistry. --- Plant genetics. --- Animal genetics. --- Statistics. --- Life Sciences. --- Biochemistry, general. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Animal Genetics and Genomics. --- Plant Genetics & Genomics. --- Bayesian statistical decision theory --- Monte Carlo method --- Biomathematics. Biometry. Biostatistics --- Mathematical statistics --- Monte Carlo method. --- Markov processes. --- Statistical methods. --- Quantitative genetics --- Monte-Carlo, Méthode de --- Markov, Processus de --- Méthodes statistiques --- EPUB-LIV-FT SPRINGER-B --- Plant Genetics and Genomics. --- Statistics . --- Plants --- Statistical analysis --- Statistical data --- Statistical science --- Mathematics --- Econometrics --- Biological chemistry --- Chemical composition of organisms --- Organisms --- Physiological chemistry --- Chemistry --- Medical sciences --- Composition --- Markov --- Méthode de Monte Carlo --- Inférence --- GENETICS --- STATISTICS --- MONTE CARLO METHOD --- MARKOV CHAINS --- STATISTICS AND NUMERICAL DATA