vectorProbs

a vector of counts, weights, or probabilities.

the node for the parameters. this is converted to a character
string. It is important that this be given accurately or it will not 
match with the code written by <code>writeNetworkModel</code>.

node

A logical indicating if the weights in <code>p</code> should
be normalized (each value is taken as a proportion of the sum).

normalize

Probability vectors can be passed manually to the model, but
 they must be formatted in code appropriate to JAGS. <code>vectorProbs</code>
 will convert a vector of counts or weights to probabilities and format
 it into JAGS code.

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.

vectorProbs: Convert a vector to JAGS Probabilities

Description

Usage

Arguments

Examples