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Several textbooks discuss inference in Bayesian networks and other kinds of prob- abilistic graphical models, such as Markov random fields and factor graphs [2]–[4], [8]–[10]. These books discuss inference methods, including belief propagation and the junction tree algorithm mentioned in Section 2.2.
Decision Making under Uncertainty. In previous lectures, we considered decision problems in which the decision maker does not know the consequences of his choices but he is given the probability of each con-sequence under each choice. In most economic applications, such a probability is not given.
Mar 23, 2023 · There are several methods for decision-making under conditions of uncertainty and risk, including: Decision Trees. Decision trees are visual representations of the decision-making process,...
Given a decision query from a user, our framework DeLLMa (Decision-making LLM assistant) aims to perform optimal decision making under uncertainty. DeLLMa consists of four main steps: Identify relevant unknown states based on the problem description and user goals.
Decision Problems: Certainty. A decision problem under certainty is: a set of decisions D. e.g., paths in search graph, plans, actions... a set of outcomes or states S. e.g., states you could reach by executing a plan. an outcome function f : D →S. the outcome of any decision. a preference ordering ≽ over S.
Aug 27, 2023 · The decision tree technique is found to be the most popular tool used for decision-making under uncertainty. I will elucidate the basic elements of a decision tree, how it works, how probability concepts are applied to approximate outcomes, and how it facilitates decision-making.
Apr 9, 2019 · Part I presents five approaches for designing strategic plans under deep uncertainty: Robust Decision Making, Dynamic Adaptive Planning, Dynamic Adaptive Policy Pathways, Info-Gap Decision Theory, and Engineering Options Analysis.
