Elements of Probabilistic Graphical Models for Machine Learning

A model is a representation of a real world phenomena. Probability theory gives us a framework to quantify uncertainty in a mathematically rigorous manner [4]. A random variable is a function on the elements in the sample space. A random variable takes on values and the act of a random variable taking on values can be described by a probability distribution. Conceptualizing entities in terms of random variables and representing the joint probability distribution over these entities is essential to statistical modelling and supervised machine learning. A graph is a data structure which consists of a collection of nodes and edges. Graphs as mathematical structures are studied in graph theory [9]. Using graphs to describe probability distributions over random variables gives us a potent way to map the flow of influence, interdependencies and independencies between the random variables. Graph based manipulations give us enhanced computational efficiency and add to the descriptive power, performance of our model. In this paper, we attempt to understand the essential concepts concerning probabilistic graphical models. We will see representation using Bayesian belief networks, Markov networks, techniques for inference and learning in probabilistic graphical models. Citation: Advait Pravin Savant. "Elements of Probabilistic Graphical Models for Machine Learning." Global Research and Development Journal For Engineering 6.2 (2021): 1 - 11.