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Case Studies in Applied Bayesian Data Science: Cirm Jean-Morlet Chair, Fall 2018

research program at CIRM in France in the second semester of 2018, which supported Kerrie Mengersen as a visiting Jean-Morlet Chair and Pierre;

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Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track

vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data;

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Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track

vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data;

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Machine Learning and Knowledge Discovery in Databases

vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data;

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Machine Learning and Knowledge Discovery in Databases

vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data;

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Machine Learning and Knowledge Discovery in Databases

vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data;

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Bayesian Artificial Intelligence

their own applied research to illustrate various applications of the technology. New to the Second Edition New chapter on Bayesian network;

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Bayesian Artificial Intelligence

errors New applications and case studies More coverage on the uses of causal interventions to understand and reason with causal Bayesian;

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Bayesian Analysis of Gene Expression Data

Bioinformatics. Case studies, illustrating Bayesian analyses of public gene expression data, provide the backdrop for students to develop analytical;

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Bayesian Models for Categorical Data

The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology;

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Bayesian Cognitive Modeling

this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.;

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Bayesian Cognitive Modeling

this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.;

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Bayesian Analysis of Stochastic Process Models

, computational, inference, forecasting, decision making and important applied models. Key features: * Explores Bayesian analysis of models based on;

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Using R for Bayesian Spatial and Spatio-Temporal Health Modeling

in applying Bayesian methodology in small area health data studies. Features: Review of R graphics relevant to spatial health data;

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Bayesian Statistical Methods

Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis;

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Bayesian Statistical Methods

Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis;

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Citizen Relationship Management

study design, a model of Citizen Relationship Management (CiRM) is developed and discussed. The case studies explore the perceptions of CRMCiRM;

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Data Science for Wind Energy

analysis, kernel regression, decision trees, kNN, splines, Bayesian inference, and importance sampling. More importantly, the data science methods;

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Data Science for Wind Energy

analysis, kernel regression, decision trees, kNN, splines, Bayesian inference, and importance sampling. More importantly, the data science methods;

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Likelihood Methods in Biology and Ecology

of these approaches in case studies. Features: Discusses the likelihood function in both Bayesian and frequentist contexts;

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Likelihood Methods in Biology and Ecology

of these approaches in case studies. Features: Discusses the likelihood function in both Bayesian and frequentist;

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Missing Data in Longitudinal Studies

sciences, this resource offers a unified Bayesian approach to handle missing data in longitudinal studies.;

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Case Studies in Bayesian Statistical Modelling and Analysis

in the implementation of these approaches. Case Studies in Bayesian Statistical Modelling and Analysis: * Illustrates how to do Bayesian analysis;

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Probabilistic Methods for Bioinformatics

explained in an accessible way using applications and case studies. The many useful applications of Bayesian networks that have been developed;

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Data Mining

This book constitutes the refereed proceedings of the 16th Australasian Conference on Data Mining, AusDM 2018, held in Bathurst, NSW;

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Substitution and Tiling Dynamics: Introduction to Self-inducing Structures

version of courses given in 2017 at the research school associated with the Jean-Morlet chair program. Tilings have been designed, used and;

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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

This book outlines Bayesian statistical analysis in great detail, from the development of a model through the process of making statistical;

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