This note aims to give a general overview of variational inference on graphical models. Starting with the need for the variational approach, we proceed to the derivation of the Variational Bayes EM algorithm that creates distributions on the hidden variables in a graphical model. This leads us to the Variational message Passing algorithm for conjugate exponential families, which is shown to res...

We study iterative randomized greedy algorithms for generating (elimination) orderings with small induced width and state space size two parameters known to bound the complexity of inference in graphical models. We propose and implement the Iterative Greedy Variable Ordering (IGVO) algorithm, a new variant within this algorithm class. An empirical evaluation using different ranking functions an...

Modern structural equation modeling software programs include user interfaces for the entry of graphical diagrams as a method for the production of the underlying matrices that are then manipulated in traditional ways to provide parameter estimates and fit statistics. This work presents an algorithm for the solution of the problem in the reverse order: automatically producing a graphical diagra...

The primary objective of this paper is to develop a new method for root-finding by combining forward and finite-difference techniques in order provide an efficient, derivative-free algorithm with lower processing cost per iteration. This will be accomplished techniques. We also detail the convergence criterion that was devised approach, we show recommended quintic-order convergent. addressed fe...

Traditional machine learning methods assume that instances are independent while in reality there are many relational datasets, such as hyperlinked web pages, scientific literatures with dependencies among citations, social networks, and more. Recent work on graphical models has demonstrated performance improvement on relational data. In my thesis I plan to study a meta-learning scheme called s...