Bringing Bayesian Models to Life

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Acknowledgments
Authors
SECTION I: Background
	Chapter 1: Bayesian Models
		1.1 Introduction and Statistical Notation
		1.2 Probability Concepts
		1.3 Modeling Concepts
		1.4 Additional Concepts and Reading
	Chapter 2: Numerical Integration
		2.1 Bayesian Integrals
		2.2 Numerical Quadrature
		2.3 Additional Concepts and Reading
	Chapter 3: Monte Carlo
		3.1 Sampling
		3.2 Monte Carlo Integration
		3.3 Additional Concepts and Reading
	Chapter 4: Markov Chain Monte Carlo
		4.1 Metropolis-Hastings
		4.2 Metropolis-Hastings in Practice
		4.3 Proposal Distributions
		4.4 Gibbs Sampling
		4.5 Additional Concepts and Reading
	Chapter 5: Importance Sampling
		5.1 Additional Concepts and Reading
SECTION II: Basic Models and Concepts
	Chapter 6: Bernoulli-Beta
		6.1 MCMC with Symmetric Proposal Distributions
		6.2 MCMC with Proposal Distributions Based on Transformations
		6.3 Additional Concepts and Reading
	Chapter 7: Normal-Normal
		7.1 Additional Concepts and Reading
	Chapter 8: Normal-Inverse Gamma
		8.1 Additional Concepts and Reading
	Chapter 9: Normal-Normal-Inverse Gamma
		9.1 Additional Concepts and Reading
SECTION III: Intermediate Models and Concepts
	Chapter 10: Mixture Models
		10.1 Additional Concepts and Reading
	Chapter 11: Linear Regression
		11.1 Additional Concepts and Reading
	Chapter 12: Posterior Prediction
		12.1 Additional Concepts and Reading
	Chapter 13: Model Comparison
		13.1 Additional Concepts and Reading
	Chapter 14: Regularization
		14.1 Additional Concepts and Reading
	Chapter 15: Bayesian Model Averaging
		15.1 Additional Concepts and Reading
	Chapter 16: Time Series Models
		16.1 Univariate Autoregressive Models
		16.2 Autoregressive Models for Populations
		16.3 Prediction with Time Series Models
		16.4 Multivariate Autoregressive Models for Animal Movement
		16.5 Additional Concepts and Reading
	Chapter 17: Spatial Models
		17.1 Geostatistical Models
		17.2 Bayesian Kriging
		17.3 Areal Data Models
		17.4 Additional Concepts and Reading
SECTION IV: Advanced Models and Concepts
	Chapter 18: Quantile Regression
		18.1 Quantile Models for Continuous Data
		18.2 Additional Concepts and Reading
	Chapter 19: Hierarchical Models
		19.1 Hierarchical Gaussian Models
		19.2 Two-Stage Model Fitting Algorithms
		19.3 Additional Concepts and Reading
	Chapter 20: Binary Regression
		20.1 Generalized Linear Models
		20.2 Logistic Regression
		20.3 Probit Regression
		20.4 Quantile Models for Binary Data
		20.5 Additional Concepts and Reading
	Chapter 21: Count Data Regression
		21.1 Poisson Regression
		21.2 Resource Selection Functions and Species Distribution Models
		21.3 Step Selection Functions
		21.4 Poisson Time Series Models
		21.5 Model Checking
		21.6 Negative Binomial Regression
		21.7 Quantile Models for Count Data
		21.8 Binomial Models
		21.9 Additional Concepts and Reading
	Chapter 22: Zero-Inflated Models
		22.1 Mixture Models for Excess Zeros
		22.2 Zero-Inflated Poisson Models
		22.3 Zero-Inflated Negative Binomial Models
		22.4 Additional Concepts and Reading
	Chapter 23: Occupancy Models
		23.1 Simple Occupancy Models
		23.2 General Occupancy Models
		23.3 Probit Occupancy Models
		23.4 Additional Concepts and Reading
	Chapter 24: Abundance Models
		24.1 Capture-Recapture Models
		24.2 Distance Sampling Models
		24.3 Survival Models
		24.4 N-Mixture Models
		24.5 Additional Concepts and Reading
SECTION V: Expert Models and Concepts
	Chapter 25: Integrated Population Models
		25.1 Data Reconciliation
		25.2 False Positive Models with Auxiliary Data
		25.3 Population Vital Rate IPMs
		25.4 Additional Concepts and Reading
	Chapter 26: Spatial Occupancy Models
		26.1 Additional Concepts and Reading
	Chapter 27: Spatial Capture-Recapture Models
		27.1 Additional Concepts and Reading
	Chapter 28: Spatio-temporal Models
		28.1 Multivariate Time Series Models
		28.2 Mechanistic Spatio-temporal Models
		28.3 Additional Concepts and Reading
	Chapter 29: Hamiltonian Monte Carlo
		29.1 Additional Concepts and Reading
Tips and Tricks
Glossary
References
Probability Distributions
Index