Bayesian Networks are a graphical representation of structuring the probabilistic model, i.e., in ways the random variables may be dependent on each other. They intuitively represent domains with a causal structure, and the edges in the graph determine which variables directly influence which other variables. It can be equivalently regarded as a representation of factorized structure of the joint probability distribution, or as encoding a set of conditional independence hypotheses on the distribution.
##### Goals:

Application of probability and statistics to Machine Learning
##### BrandIdea’s Implementation:

Product Recognition using Image Processing