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Bayesian Methods for Nonlinear Classification and Regression

Classification and Regression is the first book to bring together, in a consistent statistical framework, the ideas of nonlinear modelling and Bayesian;

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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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Regression Modeling: Methods, Theory, and Computation with SAS

, nonlinear least squares, and time-series regression issues. For most of the regression methods, the author includes SAS procedure code, enabling;

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Regression Modeling: Methods, Theory, and Computation with SAS

, nonlinear least squares, and time-series regression issues. For most of the regression methods, the author includes SAS procedure code, enabling;

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Flexible Bayesian Regression Modelling

Flexible Bayesian Regression Modeling is a step-by-step guide to the Bayesian revolution in regression modeling, for use in advanced;

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Robust Nonlinear Regression

mishandled aspects of modeling R packages for both classical and robust nonlinear regression are presented in detail in the book and on an;

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Predictive Analytics

Provides a foundation in classical parametric methods of regression and classification essential for pursuing advanced topics in predictive;

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Data-Driven Computational Neuroscience

regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts;

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Alternative Methods of Regression

presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and;

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Bayesian Methods for Repeated Measures

Measures presents the main ideas for the analysis of repeated measures and associated designs from a Bayesian viewpoint. It describes many;

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Bayesian Methods for Repeated Measures

Measures presents the main ideas for the analysis of repeated measures and associated designs from a Bayesian viewpoint. It describes many;

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Bayes Rules!

interconnected range of multivariable regression and classification models. Presents fundamental Markov chain Monte Carlo simulation techniques for;

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Support Vector Machines for Antenna Array Processing and Electromagnetics

Support Vector Machines (SVM) were introduced in the early 90's as a novel nonlinear solution for classification and regression tasks;

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Support Vector Machines for Antenna Array Processing And Electromagnetics

Support Vector Machines (SVM) were introduced in the early 90's as a novel nonlinear solution for classification and regression tasks;

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Bayesian Modeling in Bioinformatics

Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high;

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Bayesian Modeling in Bioinformatics

Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high;

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Bayesian Statistical Methods

. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized;

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Bayesian Statistical Methods

. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized;

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Adaptive Regression for Modeling Nonlinear Relationships

This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models;

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Measurement, Regression, and Calibration

specifically developed for spectroscopy. The other chapters are quite general in their applicability. Likelihood and Bayesian inference features;

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Statistical Challenges in Modern Astronomy V

methods, Poisson processes, nonlinear regression, maximum likelihood, multivariate classification, and wavelet and multiscale analyses are all;

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Learning Bayesian Models with R: Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems

patterns form data using machine learning models and use it for predicting future observations. However, learning and implementing Bayesian models;

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Machine Learning

squares and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It;

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Bayesian Ideas And Data Analysis

Emphasizing the use of WinBUGS and R to analyze real data, Bayesian Ideas and Data Analysis: An Introduction for Scientists and;

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Introduction to Bayesian Statistics

Normal mean vector; Bayesian inference for the Multiple Linear Regression Model; and Computational Bayesian Statistics including Markov Chain;

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An Introduction to Generalized Linear Models

focusing on methods for analyzing particular kinds of data. It covers normal, Poisson, and binomial distributions; linear regression models;

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