Perceptual Inference Network (PIN): Formulation 
Boyer - 21 
• Bayesian networks are directed acyclic graphs with nodes repre 
senting propositions (or random variables) and arcs signifying di 
rect dependencies as quantified by conditional probabilities. The 
conditional dependencies are made explicit by the use of a graph 
ical structure. 
• Our Problem: We have m features, / 1 , • • •, / m , at n locations 
/ 1 , • • •, / n . We need a means of efficient updating of probabilities 
of these features. Let us form our binary random variables as lj 3 . 
• Observations: 
1. Dependent features tend to be close together spatially. 
2. Similar feature types at different location will have similar 
neighborhood structure. 
• Solution: Group the random variables , • • •, l[ 3 to form a com 
posite random variable representing the events that feature fj 
occurs at lj through /*.