Nonlinear non-Gaussian state-space models are ubiquitous in statistics, econometrics,
information engineering and signal processing. Particle methods, also known as Sequential …

Nonlinear non-Gaussian state-space models arise in numerous applications in control and
signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters …

This open access book presents the first comprehensive overview of general methods in
Automated Machine Learning (AutoML), collects descriptions of existing systems based on …

Machine learning techniques have deeply rooted in our everyday life. However, since it is
knowledge-and labor-intensive to pursue good learning performance, humans are heavily …

In this paper we propose a new framework for learning from large scale datasets based on
iterative learning from small mini-batches. By adding the right amount of noise to a standard …

Summary Markov chain Monte Carlo and sequential Monte Carlo methods have emerged as
the two main tools to sample from high dimensional probability distributions. Although …

This purpose of this introductory paper is threefold. First, it introduces the Monte Carlo
method with emphasis on probabilistic machine learning. Second, it reviews the main …

Particle filtering in high dimensional state-spaces can be inefficient because a large number
of samples is needed to represent the posterior. A standard technique to increase the …

Optimal filtering problems are ubiquitous in signal processing and related fields. Except for a
restricted class of models, the optimal filter does not admit a closed-form expression. Particle …

Jump Markov linear systems (JMLS) are linear systems whose parameters evolve with time
according to a finite state Markov chain. In this paper, our aim is to recursively compute …