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Probabilistic Modeling in Bioinformatics and Medical Informatics

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Cover of 'Probabilistic Modeling in Bioinformatics and Medical Informatics'

Table of Contents

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    Book Overview
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    Chapter 1 A Leisurely Look at Statistical Inference
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    Chapter 2 Introduction to Learning Bayesian Networks from Data
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    Chapter 3 A Casual View of Multi-Layer Perceptrons as Probability Models
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    Chapter 4 Introduction to Statistical Phylogenetics
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    Chapter 5 Detecting Recombination in DNA Sequence Alignments
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    Chapter 6 RNA-Based Phylogenetic Methods
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    Chapter 7 Statistical Methods in Microarray Gene Expression Data Analysis
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    Chapter 8 Inferring Genetic Regulatory Networks from Microarray Experiments with Bayesian Networks
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    Chapter 9 Modeling Genetic Regulatory Networks using Gene Expression Profiling and State-Space Models
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    Chapter 10 An Anthology of Probabilistic Models for Medical Informatics
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    Chapter 11 Bayesian Analysis of Population Pharmacokinetic/Pharmacodynamic Models
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    Chapter 12 Assessing the Effectiveness of Bayesian Feature Selection
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    Chapter 13 Bayes Consistent Classification of EEG Data by Approximate Marginalization
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    Chapter 14 Ensemble Hidden Markov Models with Extended Observation Densities for Biosignal Analysis
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    Chapter 15 A Probabilistic Network for Fusion of Data and Knowledge in Clinical Microbiology
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    Chapter 16 Software for Probability Models in Medical Informatics
Attention for Chapter 9: Modeling Genetic Regulatory Networks using Gene Expression Profiling and State-Space Models
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