Recognition of facial expressions - Knowledge Based Systems ...

Bayesian networks were designed to encode explicitly encode “deep knowledge” rather
than heuristics, to simplify knowledge acquisition, provide a firmer theoretical ground,
and foster reusability.
The idea of Bayesian networks is to build a network of causes and effects. Each event,
generally speaking, can be certain or uncertain. When there is a new piece of evidence,
this is transmitted to the whole network and all the beliefs are updated. The research
activity in this field consists of the most efficient way of doing the calculation, using
Bayesian inference, graph theory, and numerical approximations.
The BBN mechanisms are close to the natural way of human reasoning, the initial beliefs
can be those of experts (avoiding the long training needed to set up, for example, neural
networks, unfeasible in practical applications), and they learn by experience as soon as
they start to receive evidence.
Bayes Theorem
P ( h | D)
=
P(
D | h)
P(
h)
P(
D)
In the formula,
P(h) is prior probability of hypothesis h
P(D) is prior probability of training data D
P(h | D)is probability of h given D
P(D | h) is probability of D given h
Choosing Hypotheses
Generally want the most probable hypothesis given the training data
Maximum a posteriori hypothesis h
MAP
:
h
MAP
= arg max P(
h / D)
h∈H
P(
D | h)
P(
h)
= arg max
h∈H
P(
D)
= arg
max P(
D | h)
P(
h)
h∈H
- 32 -