Normalizing flows, autoregressive models, variational autoencoders (VAEs), and deep
energy-based models are among competing likelihood-based frameworks for deep …

This book is a graduate-level introduction to probabilistic programming. It not only provides a
thorough background for anyone wishing to use a probabilistic programming system, but …

More than twenty years after its introduction, Annealed Importance Sampling (AIS) remains
one of the most effective methods for marginal likelihood estimation. It relies on a sequence …

Annealed importance sampling (AIS) and related algorithms are highly effective tools for
marginal likelihood estimation, but are not fully differentiable due to the use of Metropolis …

In variational inference (VI), the marginal log-likelihood is estimated using the standard
evidence lower bound (ELBO), or improved versions as the importance weighted ELBO …

Retrieval-augmented models have proven to be effective in natural language processing
tasks, yet there remains a lack of research on their optimization using variational inference …

Abstract Annealed Importance Sampling (AIS) synthesizes weighted samples from an
intractable distribution given its unnormalized density function. This algorithm relies on a …

Recent research has developed several Monte Carlo methods for estimating the
normalization constant (partition function) based on the idea of annealing. This means …

Thermodynamic integration (TI) offers a rigorous method for estimating free-energy
differences by integrating over a sequence of interpolating conformational ensembles …

Variational inference is a powerful paradigm for approximate Bayesian inference with a
number of appealing properties, including support for model learning and data subsampling …