Belief function theory is an appropriate framework to model different forms of knowledge including probabilistic knowledge. One simple and efficient way to ...
Belief function theory is a general framework to reason with uncertain knowledge which has connections to other frameworks such as probability, possibility and ...
Dec 12, 2011 · Belief function theory is an appropriate framework to model different forms of knowledge including probabilistic knowledge.
The main goal of this article is to review the concept of conditional belief functions in the Dempster-Shafer (DS) theory of belief functions.
Jun 11, 2020 · Belief graphical models can be built either by eliciting the uncertain knowledge of an expert or automatically learnt from data using machine.
Static: We represent knowledge using either: basic probability assignment (BPA) m, belief function Bel, plausibility function Pl, commonality function Q.
Oct 30, 2019 · It has two parts. A static part that is concerned with representation of knowledge, and a dynamic part that is concerned with reasoning with.
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The two most common types of graph- ical models are Bayesian networks (also called belief networks or causal networks) and Markov networks (also called Markov ...
Jul 4, 2023 · In belief-function directed graphical models, we construct a joint using such conditional belief functions. We begin with the probabilistic ...
Belief propagation methods use the conditional independence relationships in a graph to do efficient inference (for singly connected graphs, exponential gains ...