Arising from many applications at the intersection of decision-making and machine learning,
Marginal Maximum A Posteriori (Marginal MAP) problems unify the two main classes of …

Mixed inference such as the marginal MAP query (some variables marginalized by
summation and others by maximization) is key to many prediction and decision models. It is …

Marginal MAP inference involves making MAP predictions in systems defined with latent
variables or missing information. It is significantly more difficult than pure marginalization …

The Marginal MAP inference task is known to be extremely hard particularly because the
evaluation of each complete MAP assignment involves an exact likelihood computation (a …

Marginal MAP is a key task in Bayesian inference and decision-making. It is known to be
very difficult in general, particularly because the evaluation of each MAP assignment …

The marginal maximum a posteriori probability (MAP) estimation problem, which calculates
the mode of the marginal posterior distribution of a subset of variables with the remaining …

We introduce new anytime search algorithms that combine best-first with depth-first search
into hybrid schemes for Marginal MAP inference in graphical models. The main goal is to …

Credal networks extend Bayesian networks to allow for imprecision in probability values.
Marginal MAP is a widely applicable mixed inference task that identifies the most likely …

Probabilistic circuits (PCs) such as sum-product networks efficiently represent large multi-
variate probability distributions. They are preferred in practice over other probabilistic …

This paper explores the anytime performance of search-based algorithms for solving the
Marginal MAP task over graphical models. The current state of the art for solving this …