17 8 Deep Learning 8.1 Introduction 8.2 Gradient Descent 8.3 Stochastic Gradient Descent 8.4 Natural Gradient Descent 8.5 Neural Networks 8.6 Backpropagation 8.7 Backpropagation Through Time 8.8 Regularization 8.9 Initialization 8.10 Convolutional Neural Network 8.11 Long Short‐Term Memory 8.12 Hebbian Learning 8.13 Gibbs Sampling 8.14 Boltzmann Machine 8.15 Autoencoder 8.16 Generative Adversarial Network 8.17 Transformer 8.18 Concluding Remarks
18 9 Deep Learning‐Based Filters 9.1 Introduction 9.2 Variational Inference 9.3 Amortized Variational Inference 9.4 Deep Kalman Filter 9.5 Backpropagation Kalman Filter 9.6 Differentiable Particle Filter 9.7 Deep Rao–Blackwellized Particle Filter 9.8 Deep Variational Bayes Filter 9.9 Kalman Variational Autoencoder 9.10 Deep Variational Information Bottleneck 9.11 Wasserstein Distributionally Robust Kalman Filter 9.12 Hierarchical Invertible Neural Transport 9.13 Applications 9.14 Concluding Remarks
19 10 Expectation Maximization 10.1 Introduction 10.2 Expectation Maximization Algorithm 10.3 Particle Expectation Maximization 10.4 Expectation Maximization for Gaussian Mixture Models 10.5 Neural Expectation Maximization 10.6 Relational Neural Expectation Maximization 10.7 Variational Filtering Expectation Maximization 10.8 Amortized Variational Filtering Expectation Maximization 10.9 Applications 10.10 Concluding Remarks
20 11 Reinforcement Learning‐Based Filter 11.1 Introduction 11.2 Reinforcement Learning 11.3 Variational Inference as Reinforcement Learning 11.4 Application 11.5 Concluding Remarks
21 12 Nonparametric Bayesian Models 12.1 Introduction 12.2 Parametric vs Nonparametric Models 12.3 Measure‐Theoretic Probability 12.4 Exchangeability 12.5 Kolmogorov Extension Theorem 12.6 Extension of Bayesian Models 12.7 Conjugacy 12.8 Construction of Nonparametric Bayesian Models 12.9 Posterior Computability 12.10 Algorithmic Sufficiency 12.11 Applications 12.12 Concluding Remarks
22 References
23 Index
24 Wiley End User License Agreement
List of Tables
1 Chapter 11Table 11.1 Reinforcement learning and variational inference viewed as expect...
List of Illustrations
1 Chapter 1Figure 1.1 The encoder of an asymmetric autoencoder plays the role of a nonl...
2 Chapter 6Figure 6.1 Typical posterior estimate trajectories for: (a) sampling importa...
3 Chapter 7Figure 7.1 The SVSF state estimation concept.Figure 7.2 Effect of the smoothing subspace on chattering: (a)
