What is inference graphical model?

Given a graphical model, the most fundamental (and yet highly non-trivial) task is compute the marginal distribution of one or a few such variables. This task is usually referred to as ‘inference’.

Is variational inference Bayesian?

Variational Bayesian methods are a family of techniques for approximating intractable integrals arising in Bayesian inference and machine learning. To provide an analytical approximation to the posterior probability of the unobserved variables, in order to do statistical inference over these variables.

What type of information do graphical models present?

A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning.

What type of distribution does probabilistic inference compute?

The most common probabilistic inference task is to compute the posterior distribution of a query variable or variables given some evidence.

What is the time complexity of exact inference on graphical models?

The time complexity of exact inference on arbitrary graphical models is NP-hard. However, we can improve e\ciency for particular families of graphical models. Approximate inference techniques include stochastic simulation and sampling methods, Markov chain Monte Carlo methods, and variational algorithms.

What are the two types of inference techniques?

There are two types of inference techniques: exact inference and approximate inference. Exact inference algorithms calculate the exact value of probability P(XjY). Algorithms in this class include the elimination algorithm, the message-passing algorithm (sum-product, belief propagation), and the junction tree algo- rithms.

Is exact or approximate inference more widely used?

In practice, exact inference is not used widely, and most probabilistic inference algorithms are approximate. Nevertheless, it is important to understand exact inference and its limitations. There are two typical tasks with graphical models: inference and learning.

What is the difference between inference and learning in statistics?

However, in statistics, both inference and learning are commonly referred to as either inference or estimation. From the Bayesian perspective, for example, learning p(MjD) is actually an inference problem. When not all variables are observable, computing point estimates of Mneeds inference to impute the missing data.