Probabilistic Reasoning Flashcards
What is a Bayesian Network?
A Bayesian network is a directed acyclic graph whose nodes correspond to random variables; each node has a conditional distribution for the node, given its parents.
Why are bayesian networks useful?
Bayesian networks provide a concise way to represent conditional independence rela- tionships in the domain.
What does a Bayesian Network depict?
A Bayesian network specifies a full joint distribution; each joint entry is defined as the product of the corresponding entries in the local conditional distributions. A Bayesian network is often exponentially smaller than an explicitly enumerated joint distribution.
What are hybrid bayesian networks?
Many conditional distributions can be represented compactly by canonical families of distributions. Hybrid Bayesian networks, which include both discrete and continuous variables, use a variety of canonical distributions
How does one make inference in a Bayesian Network?
Inference in Bayesian networks means computing the probability distribution of a set of query variables, given a set of evidence variables. Exact inference algorithms, such as variable elimination, evaluate sums of products of conditional probabilities as effi- ciently as possible.
What its the time complexity of a poly tree inference?
In polytrees (singly connected networks), exact inference takes time linear in the size of the network. In the general case, the problem is intractable.
How are approximation techniques useful, as opposed to variable elimination?
Stochastic approximation techniques such as likelihood weighting and Markov chain Monte Carlo can give reasonable estimates of the true posterior probabilities in a net- work and can cope with much larger networks than can exact algorithms.
