Probabilistic Graphical Models-Knowledge Engineering

设计图模型时面临着以下选择：

Template based vs specific

Directed vs undirected

Generative vs discriminative

Hybrid（混合） model是很常见的，有时候会同时有Template based部分和非Template level的部分。：

Template-based	Hybrid	Specific
image segmentation fairly small number of variable types	fault diagnosis elements unique and shared	medical diagnosis large number of unique variables
Generative		Discriminative
no predetermined task(task shifts) not predetermined set of variables as input and disease as outputs medical diagnosis task every patient presents differently(symptoms&tests) easier to train in certain regimes(when data is not fully labeled)		a particular prediction task better solved by having richly expressive features avoid dealing with correlations between features ->high performance model
Directed		undirected
arrows correspond to causality

latent model：简化模型结构

Discriminative models directly model P(Y|X) and avoid dealing with the joint distribution. Generative models handle missing data or partially labeled data better, so they are preferable if obtaining labels is hard. High-dimensional variables are in general more difficult to model, and discriminative models avoid this by conditioning on them. Generative models can be used to generate new observable data.

Causal ordering不仅更intuitive，而且使模型更加sparse，更容易parameterize。