In this section we describe an implementation of local robust analysis for Quasi-Bayesian networks and present an example to illustrate the methods.

Local robust analysis is available in the *JavaBayes* system, a portable
and freely distributed inference engine for graphical models. *JavaBayes*
is written in Java and can run in any computing platform that supports the
Java virtual machine.
*JavaBayes* uses standard algorithms to perform calculation of
posterior marginals, expectations, maximum a posteriori explanations
and maximum a posteriori expectation.
Documentation, code and examples for *JavaBayes* can be downloaded from
http://www.cs.cmu.edu/~fgcozman/Research/JavaBayes/Home.

As an example, consider a troubleshooting problem where
the objective is to analyze the state of a car [20],
which contains 17 variables and several
deterministic and stochastic relationships.
Suppose there is some imprecision in the probability values
for two variables. First, take the variable *BatteryAge*, which
has two values, *Old* and *New*. Suppose this variable is associated
with an epsi-contaminated credal set where
epsi= 0.2 and
p(*BatteryAge**Lights*, which depends
on the binary variable *BatteryPower*. Suppose the expert
defines the conditional distribution depicted in Table 1.

BatteryPower rarr | Good | Poor |

Lights = Work | 0.8 | 0 |

Lights = NoLight | 0.2 | 1 |

BatteryPower rarr | Good | Poor |

Lights = Work | 0.944444 | 0 |

Lights = NoLight | 0.055555 | 1 |

This model can be inserted into *JavaBayes* together with arbitrary
evidence. For example, if the variable *Starts* is set to *No*,
the posterior lower bounds for the binary variable *BatteryPower*
are (0.7037, 0.2702) and the posterior upper bounds are (0.7297, 0.2963).

Fri May 30 15:55:18 EDT 1997