ashu-cybertron / bayesian_network

Bayesian_Network

AIM: To construct a Bayesian Network Considering medical data.use this model to demonstrate the diagnosis heart patient using standard heart disease dataset.

https://github.com/sahanabalappa/Bayesian_Network/blob/main/Bayesian_Network.ipynb

Introduction:

Bayesian_Network is a probabilistic graphical model that represents a set of variables and conditional dependencies via a directed acyclic graph(DAG).

bake()

Finalize the topology of the model.

Assign a numerical index to every state and create the underlying arrays corresponding to the states and edges between the states.

This method must be called before any of the probability-calculating methods.

predict_proba()

Returns the probabilities of each variable in the graph given evidence.

This calculates the marginal probability distributions for each state given the evidence provided through loopy belief propagation

Parameters: data:dict

The evidence supplied to the graph. This can either be a dictionary with keys being state names and values being the observed values .

Returns: probabilities:array-like, shape (n_nodes)

An array of univariate distribution objects showing the probabilities of each variable.