|Title:||A Review on Probabilistic Graphical Models and Tools|
|Keywords:||Probabilistic Graphical Model, Probabilistic Relational Model, Bayes Theorem, Bayesian Network, Object- Oriented Bayesian Network|
Our daily life is full of challenges, and the biggest challenge is the unpredictability of many of our significant life events. To deal with this unpredictability, analysing the probability of events has become very important. In particular, the theorem of English statistician Thomas Bayes has been revolutionary. Numerous theories and techniques have been proposed, and many tools have been developed to solve real-life problems based on the theorem, yet it is still very much an area of active research. It still attracts researchers dealing with cutting-edge technologies. One tool that has been used extensively in modelling probabilistic analysis for decades is the Probabilistic Graphical Model (PGM). PGMs have very challenging childhood but glorious youth. The vast applicability of the models in cutting-edge technologies attracts researchers, modellers and scientists of diversified fields. Hence there are numerous models with their respective features, merits and backlogs. To date, there have been very few surveys conducted among the wide range of models and their associated tools. More specifically, those few reviews are highly application and domain focused, and limited to three to four very popular and widely used models and their associated learning and inference algorithms. To the best of our knowledge, this paper is the first that presents the features, limitations, design and implementation platforms, research challenges and applicability of the models based on a common framework that consists of some essential attributes of the popular PGMs and tools for probabilistic analysis. The study helps deciding an appropriate tool as per the perspective of the application and feature of the tool. This paper concludes with future research scope and a non-exhaustive list of applications of PGMs.