References
Note
8 Bayesian Inference with Adaptive Markov Chain Monte Carlo
1 Introduction
2 Random‐Walk Metropolis Algorithm
3 Adaptation of Random‐Walk Metropolis
4 Multimodal Targets with Parallel Tempering
5 Dynamic Models with Particle Filters
6 Discussion
Acknowledgments
Notes
References
9 Advances in Importance Sampling
1 Introduction and Problem Statement
2 Importance Sampling
3 Multiple Importance Sampling (MIS)
4 Adaptive Importance Sampling (AIS)
Acknowledgments
Notes
References
8 Part III: Statistical Learning 10 Supervised Learning 1 Introduction 2 Penalized Empirical Risk Minimization 3 Linear Regression 4 Classification 5 Extensions for Complex Data 6 Discussion References 11 Unsupervised and Semisupervised Learning 1 Introduction 2 Unsupervised Learning 3 Semisupervised Learning 4 Conclusions Acknowledgment Notes References 12 Random Forests 1 Introduction 2 Random Forest (RF) 3 Random Forest Extensions 4 Random Forests of Interaction Trees (RFIT) 5 Random Forest of Interaction Trees for Observational Studies 6 Discussion References 13 Network Analysis 1 Introduction 2 Gaussian Graphical Models for Mixed Partial Compositional Data 3 Theoretical Properties 4 Graphical Model Selection 5 Analysis of a Microbiome–Metabolomics Data 6 Discussion References 14 Tensors in Modern Statistical Learning 1 Introduction 2 Background 3 Tensor Supervised Learning 4 Tensor Unsupervised Learning 5 Tensor Reinforcement Learning 6 Tensor Deep Learning Acknowledgments References 15 Computational Approaches to Bayesian Additive Regression Trees 1 Introduction 2 Bayesian CART 3 Tree MCMC 4 The BART Model 5 BART Example: Boston Housing Values and Air Pollution 6 BART MCMC 7 BART Extentions 8 Conclusion References
9 Part IV: High‐Dimensional Data Analysis 16 Penalized Regression 1 Introduction 2 Penalization for Smoothness 3 Penalization for Sparsity 4 Tuning Parameter Selection References 17 Model Selection in High‐Dimensional
