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Symbolic Computation for Statistical Inference

expression numerically over a given range. This book summarizes a decade of research into the use of symbolic computation applied to statistical;

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Computation of Multivariate Normal and t Probabilities

Multivariate normal and t probabilities are needed for statistical inference in many applications. Modern statistical computation packages;

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Statistical Modeling and Computation

integrated treatment of Mathematical Statistics and modern statistical computation, emphasizing statistical modeling, computational techniques, and;

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Statistical Inference via Convex Optimization

of their statistical performance are given by efficient computation rather than by analytical derivation typical of more conventional statistical;

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Core Statistics

parametric statistical models, including both theory and practical numerical computation. The book considers both frequentist maximum likelihood and;

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Core Statistics

parametric statistical models, including both theory and practical numerical computation. The book considers both frequentist maximum likelihood and;

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An Introduction to Statistical Inference and Its Applications with R

of the methods of statistical inference for students who are comfortable with mathematical notation. Numerous examples, case studies, and;

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An Introduction to Statistical Inference and Its Applications with R

of the methods of statistical inference for students who are comfortable with mathematical notation. Numerous examples, case studies, and;

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Introductory Statistical Inference with the Likelihood Function

This textbook covers the fundamentals of statistical inference and statistical theory including Bayesian and frequentist approaches and;

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Nonparametric Statistical Methods Using R

nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation. They illustrate the;

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Nonparametric Statistical Methods Using R

general linear and nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation;

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Nondeductive Inference

deductive models. In particular, inductive inference, predictive inference, statistical inference, and decision making are examined in some detail;

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Nondeductive Inference

deductive models. In particular, inductive inference, predictive inference, statistical inference, and decision making are examined in some detail;

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Computer Age Statistical Inference

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data;

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Essentials of Statistical Inference

approaches to inference, as well as more advanced material on developments in statistical theory, including: material on Bayesian computation, such;

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Essentials of Statistical Inference

approaches to inference, as well as more advanced material on developments in statistical theory, including: material on Bayesian computation, such;

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Quantum Computation and Information

contains fundamental areas, quantum query complexity, quantum statistical inference, quantum cloning, quantum entanglement, additivity. It treats;

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An Introduction to Bayesian Inference, Methods and Computation

These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy;

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Semimartingales and their Statistical Inference

applications to engineering and economic systems, financial economics, and the biological and medical sciences have made statistical inference for;

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Semimartingales and Their Statistical Inference

applications to engineering and economic systems, financial economics, and the biological and medical sciences have made statistical inference for;

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Introduction to the Theory of Statistical Inference

of statistical inference theory, focusing on the fundamental classical principles. Suitable for a second-semester undergraduate course on statistical;

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Introduction to the Theory of Statistical Inference

of statistical inference theory, focusing on the fundamental classical principles. Suitable for a second-semester undergraduate course on statistical;

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Optimal Statistical Inference in Financial Engineering

Until now, few systematic studies of optimal statistical inference for stochastic processes had existed in the financial engineering;

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Inferential Models

A New Approach to Sound Statistical Reasoning Inferential Models: Reasoning with Uncertainty introduces the authors' recently;

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Inferential Models

A New Approach to Sound Statistical Reasoning Inferential Models: Reasoning with Uncertainty introduces the authors' recently developed;

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Statistical Optimization for Geometric Computation

This text for graduate students discusses the mathematical foundations of statistical inference for building three-dimensional models from;

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Statistical Inference for Fractional Diffusion Processes

economics. In model building, statistical inference for stochastic processes is of great importance from both a theoretical and an applications;

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