Majid Baseer The conditional probability table (CPT) for a child node in a Bayesian network represents the likelihood of different values for the child node given different combinations of values for its parent nodes. The CPT is commonly represented as a table, with rows referring to different combinations of parent node values and columns corresponding to different child node values.

You will need the following information to compute the CPT for a child node:

1. Given no information about the parent nodes, the probability distribution for the kid node (i.e., the "prior" probability). This may be computed by dividing the total number of data points by the number of times each value for the child node appears in the data.

2. The parent node's probability distribution. This may be determined in the same way as the child node: count the number of times each combination of parent node values occurs in the data and divide by the total number of data points.

3. The probability distribution for the child node given various combinations of parent node values. This may be estimated by counting the number of times each combination of child and parent node values appears in the data and dividing by the number of times the matching combination of parent node values appears.

You may use these probability distributions to fill in the CPT for the child node once you have them. For example, if the CPT includes two rows (corresponding to two potential values for the parent node) and three columns (corresponding to three possible values for the child node), you would fill out the CPT as follows:

- The probability of the first value for the child node given the first combination of values for the parent nodes would be the entry for the first row and first column.

- The probability of the second value for the child node given the initial combination of values for the parent nodes would be the entry for the first row and second column.

- The probability of the third value for the child node given the initial combination of values for the parent nodes would be the entry for the first row and third column.

- The probability of the first value for the child node given the second combination of values for the parent nodes would be the entry for the second row and first column.

And so forth.

I hope this was helpful! Please let me know if you have any more queries.

Dear Majid Baseer,

Bayesian networks are models that consist of two parts:

A qualitative part to represent the dependencies using a Directed Acyclic Graph (DAG). A quantitative part, using local probability distributions, for specifying the probabilistic relationships.

https://library.bayesia.com/articles/#!bayesialab-knowledge-hub/key-concepts-conditional-probability-table

Regards,

Shafagat

In Bayesian networks, the probability of a child node (CPT) is calculated based on the probabilities of its parent nodes. The CPT is represented as a table that lists the probability of each possible state of the child node for each combination of states of its parent nodes.

The process of calculating the CPT for a child node in a Bayesian network typically involves the following steps:

It's worth noting that the exact process of calculating the CPT will depend on the specific characteristics of the Bayesian network and the data you are working with. Additionally, there are some methods which can be used to estimate the CPT's values when you don't have enough data, such as Laplace smoothing and Expectation-Maximization.