The Monte Carlo evaluation of path integrals is one of a few general purpose methods to
approach strongly coupled systems. It is used in all branches of physics, from QCD and …

Particle filters contain the promise of fully nonlinear data assimilation. They have been
applied in numerous science areas, including the geosciences, but their application to high …

Observing and timing a group of millisecond pulsars with high rotational stability enables the
direct detection of gravitational waves (GWs). The GW signals can be identified from the …

This open-access textbook's significant contribution is the unified derivation of data-
assimilation techniques from a common fundamental and optimal starting point, namely …

An introduction to a broad range of topics in deep learning, covering mathematical and
conceptual background, deep learning techniques used in industry, and research …

History Markov chain Monte Carlo (MCMC) methods, in which plausible values for unknown
quantities are simulated from their appropriate probability distribution, have revolutionised …

Hamiltonian dynamics can be used to produce distant proposals for the Metropolis
algorithm, thereby avoiding the slow exploration of the state space that results from the …

A general framework for constructing and using probabilistic models of complex systems that
would enable a computer to use available information for making decisions. Most tasks …

Bayesian Data Analysis describes how to conceptualize, perform, and critique statistical
analyses from a Bayesian perspective. Using examples largely from the authors' own …

Artificial" neural networks" are widely used as flexible models for classification and
regression applications, but questions remain about how the power of these models can be …