Sequential latent variable models (SLVMs) are essential tools in statistics and machine
learning, with applications ranging from healthcare to neuroscience. As their flexibility …

Bayesian phylogenetic inference is often conducted via local or sequential search over
topologies and branch lengths using algorithms such as random-walk Markov chain Monte …

Abstract Sequential Monte Carlo (SMC) is an inference algorithm for state space models that
approximates the posterior by sampling from a sequence of target distributions. The target …

Latent variable models have been widely applied for the analysis of time series resulting
from experimental neuroscience techniques. In these datasets, observations are relatively …

Time series model inference can be divided into modeling and optimization. Sequential
VAEs have been studied as a modeling technique. As an optimization technique, methods …

Reconstructing jets, which provide vital insights into the properties and histories of
subatomic particles produced in high-energy collisions, is a main problem in data analyses …

Variational inference (VI) combined with Bayesian nonlinear filtering produces state-of-the-
art results for latent time-series modeling. A body of recent work has focused on sequential …

This thesis develops variational Bayesian methods for applications in continual learning,
multi-source inference, and time-series forecasting. For continual learning, recursive …

A core goal of modeling is to help us understand the world around us, but often the
phenomena we wish to model are only observed indirectly. For example, we often detect …

Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetic Inference Page 1
Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetic Inference Antonio …