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 …

Decision making is hard. It often requires reasoning about uncertain environments, partial
observability and action spaces that are too large to enumerate. In such tasks decision …

We present classical and quantum algorithms for approximating partition functions of
classical Hamiltonians at a given temperature. Our work has two main contributions: first, we …

A commonly used approach to compute the error rate at the primary outputs (POs) of a circuit
is to compare the fault-free and faulty copies of the circuit using XOR gates. This model …

Marginal MAP problems are known to be very difficult tasks for graphical models and are so
far solved exactly by systematic search guided by a join-tree upper bound. In this paper, we …

Bucket Elimination (BE) is a universal inference scheme that can solve most tasks over
probabilistic and deterministic graphical models exactly. However, it often requires …

Spatial evolutionary games are used to model large systems of interacting agents. In earlier
work, a method was developed using Bayesian Networks to approximate the population …

Probabilistic graphical models have emerged as a powerful modeling tool for several real-
world scenarios where one needs to reason under uncertainty. A graphical model's partition …

Probabilistic graphical models offer a powerful framework to account for the dependence
structure between variables, which is represented as a graph. However, the dependence …

Performing efficient inference on high dimensional discrete Bayesian Networks (BNs) is
challenging. When using exact inference methods the space complexity can grow …