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Statistics for Astrophysics

Computation (ABC) and Bayesian nonparametric inference and clustering.;

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Statistical Inference

author then thoroughly discusses the use of the multinomial model and noninformative Dirichlet priors in model-free or nonparametric Bayesian;

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Statistical Inference

author then thoroughly discusses the use of the multinomial model and noninformative Dirichlet priors in model-free or nonparametric Bayesian;

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Fundamentals of Nonparametric Bayesian Inference

Explosive growth in computing power has made Bayesian methods for infinite-dimensional models - Bayesian nonparametrics - a nearly;

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Nonparametric Statistical Methods And Related Topics

, linear model theory, Bayesian nonparametrics, change point problems, time series analysis and asymptotic theory.This volume presents state-of-the;

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Mathematical Foundations of Infinite-Dimensional Statistical Models

of decision theory. This includes the basic theory of convolution kernel and projection estimation, but also Bayesian nonparametrics and nonparametric;

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Handbook Of Applied Econometrics And Statistical Inference

Summarizing developments and techniques in the field, this reference covers sample surveys, nonparametric analysis, hypothesis testing;

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Handbook Of Applied Econometrics And Statistical Inference

Summarizing developments and techniques in the field, this reference covers sample surveys, nonparametric analysis, hypothesis testing;

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Probability and Bayesian Modeling

Probability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus;

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Predictive Inference

of approaches to prediction including those based on parametric, nonparametric, and nonstochastic statistical models, it is devoted mainly to predictive;

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Predictive Inference

of approaches to prediction including those based on parametric, nonparametric, and nonstochastic statistical models, it is devoted mainly to predictive;

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Bayesian Modeling in Bioinformatics

differential gene expression, and identifying biomarkers. It develops novel Bayesian nonparametric approaches for bioinformatics problems, measurement;

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Bayesian Modeling in Bioinformatics

differential gene expression, and identifying biomarkers. It develops novel Bayesian nonparametric approaches for bioinformatics problems, measurement;

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Nonparametric Inference on Manifolds

, nonparametric Bayesian theory is adapted and extended to manifolds for the purposes of density estimation, regression and classification. Ideal for;

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Nonparametric Inference On Manifolds

, nonparametric Bayesian theory is adapted and extended to manifolds for the purposes of density estimation, regression and classification. Ideal for;

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An Introduction to Bayesian Inference in Econometrics

This is a classical reprint edition of the original 1971 edition of An Introduction to Bayesian Inference in Economics. This historical;

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Bayesian Data Analysis

Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis Now in its third edition, this classic book;

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Nonparametric Inference

This book provides a solid foundation on nonparametric inference for students taking a graduate course in nonparametric statistics and;

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Bayesian Analysis in Natural Language Processing

inference, Bayesian estimation, and nonparametric modeling. In response to rapid changes in the field, this second edition of the book includes a;

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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 Inference for Probabilistic Risk Assessment

Bayesian Inference for Probabilistic Risk Assessment provides a Bayesian foundation for framing probabilistic problems and performing;

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Bayesian Thinking, Modeling and Computation

point of view. It further describes parametric and nonparametric Bayesian methods for modelling and how to use modern computational methods to;

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An Introduction to Bayesian Inference and Decision

An Introduction to Bayesian Inference and Decision is een boek van Robert L Winkler;

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Introduction to Bayesian Statistics

...this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels. It is a well-written;

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Likelihood & Bayesian Inference

likelihood ratio and the Wald statistic. In the second part of the book, likelihood is combined with prior information to perform Bayesian inference;

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Likelihood and Bayesian Inference

likelihood ratio and the Wald statistic. In the second part of the book, likelihood is combined with prior information to perform Bayesian inference;

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Computational Bayesian Statistics

Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that;

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