What are the characteristics of Bayesian Belief Networks?

The naıve Bayesian classifier creates the assumption of class conditional independence, i.e., given the class label of a tuple, the values of the attributes are considered to be conditionally separate from one another. This defines evaluations.

When the assumption affects true, hence the naïve Bayesian classifier is effective in contrast with multiple classifiers. It can represent joint conditional probability distributions.

They enable class conditional independencies to be represented among subsets of variables. They support a graphical structure of causal relationships, on which learning can be implemented. Trained Bayesian belief networks are used for classification. Bayesian belief networks are also called belief networks, Bayesian networks, and probabilistic networks.

A belief network is represented by two components including a directed acyclic graph and a group of conditional probability tables. Every node in the directed acyclic graph defines a random variable. The variables can be discrete- or continuous-valued.

They can correspond to certain attributes given in the information or to “hidden variables” believed to form a relationship (e.g., in the case of medical records, a hidden variable can denote a syndrome, describing several symptoms that, together, identify a definite disease).

There are the characteristics of Bayesian Belief Networks which are as follows −

BBN supports a method for capturing the previous knowledge of a specific domain utilizing a graphical model. The network can be used to encrypt causal dependencies between variables.

It can be building the network can be time-consuming and needed a huge amount of effort. But the structure of the network has been decided, inserting a new variable is quite simple.

Bayesian networks apply to dealing with an inadequate record. Instances with missing attributes can be handled by summing or integrating the probabilities over all possible values of the attribute.

Due to the record being combined probabilistically with previous knowledge, the approach is powerful to model overfitting.

Belief networks can be used to model several well-known issues. An example is genetic linkage analysis such as the mapping of genes onto a chromosome. By casting the gene linkage issues in the method of inference on Bayesian networks, and utilizing state-of-the-art algorithms, the scalability of analysis has advanced significantly.

Several applications have benefited from the need for belief networks such as computer vision, image restoration, and stereo vision, files and text analysis, decision support systems, and sensitivity analysis. The text with which multiple applications can be reduced to Bayesian network inference is beneficial in that it curbs the need to make specialized algorithms for every application.