What is approximate inference in machine learning?

From Wikipedia, the free encyclopedia. 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.

What is approximate inference in Bayesian networks?

Abstract. Computing posterior and marginal probabilities constitutes the backbone of almost all inferences in Bayesian networks. These computations are known to be intractable in general, both to compute exactly and to approximate (e.g., by sampling algorithms).

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 exact inference?

Exact inference algorithms calculate the exact value of probability P(X|Y ). Algorithms in this class include the elimination algorithm, the message-passing algorithm (sum-product, belief propagation), and the junction tree algo- rithms. The time complexity of exact inference on arbitrary graphical models is NP-hard.

What is tractable distribution?

A distribution is called tractable if any marginal probability induced by it can be computed in linear time.

How the compactness of the Bayesian network can be described?

Explanation: If a bayesian network is a representation of the joint distribution, then it can solve any query, by summing all the relevant joint entries. Explanation: The compactness of the bayesian network is an example of a very general property of a locally structured system.

What is variational approximation?

Variational approximations is a body of deterministic tech- niques for making approximate inference for parameters in complex statistical models. 2009) emerged with claims of being able to handle a wide variety of statistical problems.

What is variational em?

The variational EM gives us a way to bypass computing the partition function and allows us to infer the parameters of a complex model using a deterministic optimization step.

What is the consequence between a node and its predecessors?

Explanation: The semantics to derive a method for constructing bayesian networks were led to the consequence that a node can be conditionally independent of its predecessors.

What is Bayesian belief network in machine learning?

Bayesian Belief Network is a graphical representation of different probabilistic relationships among random variables in a particular set. It is a classifier with no dependency on attributes i.e it is condition independent.

What is approximate inference?

Approximate inference techniques include stochastic simulation and sampling methods, Markov chain Monte Carlo methods, and variational algorithms. Lecture 4: Exact Inference 3

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.

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 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.

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