Probabilistic Networks and Expert Systems

By Shane P. Pederson. Technometrics, 2001

1,972 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f5e135219b4c9598d62d96991f0d3f63ca4cdf54

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Citations in the corpus

Convergent Tree-Reweighted Message Passing for Energy Minimization (2006, unknown): “E(x | θ) = E(x | θ̄) for all x ∈ X ) then θ is called a reparameterization of θ̄ [5,25,26,30,32] (in [25] this notion was called equivalent transformations).”
A hierarchical Bayesian network for event recognition of human actions and interactions (2004, background): “Causal relations and conditional dependencies among the random variables are efficiently represented by a directed acyclic graph (DAG) in a BayesianNetwork (BN) [5,12,13,22,27].”
An experimental design for the development of adaptive treatment strategies (2005, background): “In this case, the ascertainment of the optimal or best adaptive treatment strategy is an optimization problem treated at great length by many authors (Bellman, 1957; Sutton and Barto, 1998; Cowell, Dawid, Lauritzen and Spiegelhalter (1999)).”
Max-Margin Markov Networks (2003, background): “Nevertheless, in many classification problems, such as sequences and other graphs with low tree-width [4], the extended QP can be solved efficiently.”
Identifying the consequences of dynamic treatment strategies: A decision-theoretic overview (2010, methodology): “We shall use the theory and notation of Cowell et al. (1999) and Dawid (2002) in relation to directed acyclic graphs (DAGs) and IDs, and their application to probability and decision models.”
Partition MCMC for Inference on Acyclic Digraphs (2015, unknown): “ay want to learn a Bayesian network Bwhich best explains a set of independent observations Dfrom the distribution of the variables in X. The estimation of a Bayesian network breaks up into two steps (Cowell et al., 2007), referred to as structure and parameter learning. Given the data Dwe wish to le”
How to define and interpret a probability in a risk and safety setting (2013, unknown): “Mellor (2005), Hacking (2001) and SEP (2009).”
Parameter Priors for Directed Acyclic Graphical Models and the Characteriration of Several Probability Distributions (1999, unknown): “Cowell et al. (1999, pp. 191-2) highlight this point. The assumptions we have made lead to the following signi cant consequence: When we specify a parameter prior p( mcjm h c ) for one complete DAG model mc, we also implicitly specify a prior p( mjm ) for any DAG model m among the super exponentiall”
Classification in Networked Data: A Toolkit and a Univariate Case Study (2007, unknown): “An important alternative to heuristic (“loopy”) belief propagation is the junction-tree algorithm (Cowell et al., 1999), which provides exact solutions for arbitrary graphs.”