inputCPTExample

<p>This is an example of the output generated by the <code>inputCPT</code> function as 
illustrated in the article being submitted to JSS.  It is saved as an object 
named <code>h</code> in the article.</p>

datasets

Facilities for easy implementation of hybrid Bayesian networks
using R. Bayesian networks are directed acyclic graphs representing joint
probability distributions, where each node represents a random variable and
each edge represents conditionality. The full joint distribution is therefore
factorized as a product of conditional densities, where each node is assumed
to be independent of its non-descendents given information on its parent nodes.
Since exact, closed-form algorithms are computationally burdensome for inference
within hybrid networks that contain a combination of continuous and discrete
nodes, particle-based approximation techniques like Markov Chain Monte Carlo
are popular. We provide a user-friendly interface to constructing these networks
and running inference using the 'rjags' package. Econometric analyses (maximum
expected utility under competing policies, value of information) involving
decision and utility nodes are also supported.

Benjamin Nutter

HydeNet

Hybrid Bayesian Networks Using R and JAGS

inputCPTExample function

<p>An object of class <code>cpt</code> (inherits from <code>array</code>) with 2 rows and 2 columns.</p>

inputCPTExample: Example Conditional Probability Table Resulting from the `inputCPT` function.

Description

Usage

Arguments

Format