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Towards Bayesian Model-Based Demography

unique and novel methodology for creating empirically grounded agent-based models of international migration - one of the most uncertain;

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Towards Bayesian Model-Based Demography

unique and novel methodology for creating empirically grounded agent-based models of international migration - one of the most uncertain;

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Bayesian Model Selection and Statistical Modeling

, such as Markov chain Monte Carlo. He covers the different types of simulation-based Bayesian model selection criteria, including the numerical;

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Bayesian Model Selection and Statistical Modeling

different types of simulation-based Bayesian model selection criteria, including the numerical calculation of Bayes factors.;

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Statistical Inference

solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical;

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Statistical Inference

solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical;

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Bayesian Model Comparison

implementation of parameter and non-parameter-based approaches to variable selection, a survey of key results in objective Bayesian model selection;

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Model-Based Demography

. This book shows how demography can build a strong theoretical edifice on its broad and deep empirical foundation by adoption of the model-based;

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Bayesian Networks for Managing Learner Models in Adaptive Hypermedia Systems

Managing Learner Models in Adaptive Hypermedia Systems: Emerging Research and Opportunities is a collection of research on the use of Bayesian;

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Understanding Computational Bayesian Statistics

formula giving its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are;

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Bayesian Deconvolution of Sparse Processes

-Gaussian model. Methods based on the expectation-maximization algorithm are investigated for both models, and several statistical inference and;

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Handbook of Approximate Bayesian Computation

where standard Bayesian techniques based on working with tractable likelihood functions would not be viable. ABC methods finesse the problem;

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Understanding the Tripartite Approach to Bayesian Divergence Time Estimation

describe the tripartite model used for Bayesian estimation of time calibrated phylogenetic trees. The model can be readily separated into its;

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Bayesian Statistics for Experimental Scientists

This book offers an introduction to the Bayesian approach to statistical inference, with a focus on nonparametric and distribution-free;

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Bayesian Item Response Modeling

statistical methods for - timating model parameters and evaluating model t. However, the Bayesian methodology has shown great potential, particularly;

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Bayesian Networks In Fault Diagnosis

, eleven important issues in BN-based fault diagnosis methodology, such as BN structure modeling, BN parameter modeling, BN inference, fault;

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Bayesian Methods in Epidemiology

regression, and nonlinear models. The text also introduces a Bayesian approach for the estimation of survival by life tables and illustrates other;

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Bayesian Methods in Epidemiology

/ordinal regression, and nonlinear models. The text also introduces a Bayesian approach for the estimation of survival by life tables and;

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Modeling and Reasoning with Bayesian Networks

discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design;

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Modeling and Reasoning with Bayesian Networks

discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design;

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Likelihood Free Methods for Cognitive Science

. Simulation-based models are now very popular in cognitive science, as are Bayesian methods for performing parameter inference. As such, the recent;

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Bayesian Statistics for the Social Sciences

testing, model building, and linear regression analysis, carefully explaining the differences between the Bayesian and frequentist approaches;

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An Introduction to Bayesian Inference, Methods and Computation

and principles of Bayesian inference, including the reasoning behind the prior/likelihood model construction synonymous with Bayesian methods;

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Development of Clinical Decision Support Systems using Bayesian Networks

For the development of clinical decision support systems based on Bayesian networks, Mario A. Cypko investigates comprehensive expert;

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Statistics for Astrophysics

in their field. This covers the foundations of Bayesian inference, Markov chain Monte Carlo technique, model building, Approximate Bayesian;

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Likelihood & Bayesian Inference

methods. It includes a separate chapter on modern numerical techniques for Bayesian inference, and also addresses advanced topics, such as model;

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Likelihood and Bayesian Inference

methods. It includes a separate chapter on modern numerical techniques for Bayesian inference, and also addresses advanced topics, such as model;

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