继续啃PRML
第八章：
Basic notation:
node –> random variable or group of random variables
link –> probabilistic relation ship
notation of random var and non-random var, observed and unobserved var
Conditional Independence:
three example: block means independent conditionally
1. tail-tail, observed(block), unobserved(unblock)
2. head-tail, observed(block), unobserved(unblock)
3. head-head, observed(unblock), unobserved(block)
D-separation theorm: regrard graph as filter for distribution p(x)
Markov blanket/Markov boundary
Directed graphical model –> Bayesian Networks:
Discrete variables: three ways to control number of parameters
1. chain nodes
2. sharing parameters
3. model with latent parameter
Continues variables: Linear-Gaussian model
Undirected graphical model –> Markov random field：
conditional independence property in undirected graph
factorization property in directed graph
potential function and energy function
how to convert directed graph to undirected and vice versa
I map, D map, perfect map
Inference:
chain: using potential function, local messages pass to get an efficient algorithm
trees: undirected tree, directed tree, polytree, use efficient algorithm in a broader situation
factor graph:
translate directed and undirected graph to factor graph to become tree
sum-product algorithm: read it later
max-sum algorithm: read it later
important view:
- Basic notation
- Conditional property and factorization property
- Directed graphical model –> Bayesian Networks, Undirected graphical model –> Markov random field
- Inference in graphical model