Bayesian networks are among the most prominent graphical models for probability distributions.

For this scenario, one is asked to partition the vertices into clusters so that the "highly connected" vertices belong to the same cluster.

To address the error propagation problem, we identify image fusion as the key point for improvement.

This paper studies adversarial bandits with corruptions.

There can be no loops or directed cycles. See Figure 1A for an example. The results also hold for Maximal Ancestral Graphs (MAG) [Richardson and Spirtes, 2002] without selection variables. Kinships are defined as usual: parents Our approach does not involve modified graph constructions as in van der Zander et al. [2019] and other works. A node is an ancestor and descendant of itself, but not a parent/child/spouse of itself.

One limitation in our framework is related to data collection.

A mixture of multinomial logits (MMNL) generalizes the single logit model, which is commonly used in predicting the probabilities of different outcomes.

In this paper, we propose a methodology designed to support decision-making during the execution phase of military ground combat operations, with a focus on one's actions. This methodology generates and evaluates recommendations for various courses of action for a mechanized battalion, commencing with an initial set assessed by their anticipated outcomes. It systematically produces thousands of individual action alternatives, followed by evaluations aimed at identifying alternative courses of action with superior outcomes. These alternatives are appraised in light of the opponent's status and actions, considering unit composition, force ratios, types of offense and defense, and anticipated advance rates. Field manuals evaluate battle outcomes and advancement rates. The processes of generation and evaluation work concurrently, yielding a variety of alternative courses of action. This approach facilitates the management of new course generation based on previously evaluated actions. As the combat unfolds and conditions evolve, revised courses of action are formulated for the decision-maker within a sequential decision-making framework.

To address the error propagation problem, we identify image fusion as the key point for improvement.