Answer / Singh Pritam Jitendra

Bayesian Networks (BN) and Markov Models (MM) are both probabilistic graphical models but have some key differences. BNs represent conditional probabilities among a set of variables using directed acyclic graphs, which allow for more complex relationships between variables. In contrast, MMs are represented by state transition matrices that describe the probability of each state given the current and previous states. BNs can handle causality and conditional dependencies better than MMs but may be computationally intensive due to their graphical structure.

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