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Approximate inference
Approximate
inference
methods
make it possible to
learn
realistic
models
from
big data
by trading off
computation time
for accuracy, when
exact
learning
and inference are
computationally intractable
.
Major methods
classes
Variational
Bayesian methods
Markov
chain
Monte Carlo
Expectation propagation
Markov
random fields
Bayesian networks
*Variational
message passing
Loopy
and
generalized belief propagation