Peter Hooper

Learn More
A Bayesian belief network models a joint distribution over variables using a DAG to represent variable dependencies and network parameters to represent the conditional probability of each variable given an assignment to its immediate parents. Existing algorithms assume each network parameter is fixed. From a Bayesian perspective, however, these network(More)
A Bayesian belief network is a model of a joint distribution over a finite set of variables , with a DAG structure representing immediate dependencies among the variables. For each node, a table of parameters (CP-table) represents local conditional probabilities , with rows indexed by conditioning events (assignments to parents). CP-table rows are usually(More)
A Bayesian belief network models a joint distribution with an directed acyclic graph representing dependencies among variables and network parameters characterizing conditional distributions. The parameters are viewed as random variables to quantify uncertainty about their values. Belief nets are used to compute responses to queries; i.e., conditional(More)
  • 1