What is Bayes rule explain Bayesian network?
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What is Bayes rule explain Bayesian network?
A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG).
What is Bayesian network explain how it is used to represent knowledge?
A Bayesian network (BN) is a probabilistic graphical model for representing knowledge about an uncertain domain where each node corresponds to a random variable and each edge represents the conditional probability for the corresponding random variables [9].
What type of data structure is a Bayesian network?
Bayesian networks are a structured knowledge representation, where domain variables are regarded as nodes in a graph whose structure encodes the dependencies between them. A crucial aspect is learning the dependency graph of a Bayesian network from data.
Which type of problem can be solved using Bayesian network?
Bayesian belief network is key computer technology for dealing with probabilistic events and to solve a problem which has uncertainty.
How do we perform Bayesian classification when some features are missing?
How do we perform Bayesian classification when some features are missing? (D) Drop the features completely. Answer: Option-C. Explanation: Here we don’t use general methods of handling missing values instead we integrate the posterior probabilities over the missing features for better predictions.
Which are the ways to represent uncertainty?
______________ is/are the way/s to represent uncertainty. Explanation: Entropy is amount of uncertainty involved in data. Represented by H(data).
What kind of problems do we solve using Bayesian belief networks?
It can also be used in various tasks including prediction, anomaly detection, diagnostics, automated insight, reasoning, time series prediction, and decision making under uncertainty. Bayesian Network can be used for building models from data and experts opinions, and it consists of two parts: Directed Acyclic Graph.