Variational Inference for Bayesian Nonnegative Matrix Factorisation

As an alternative approach to markov chain monte carlo, variational inference approximates the posterior distribution using an optimisation approach. Specifically, given a family of probability distributions, variational inference aims to find a member which is most similar to the posterior distribution. In this research project, I explore an extension of variational inference, the structured stochastic variational inference. I aim to develop a novel structured stochastic variational inference algorithm for a sparse non-negative matrix factorisation model and apply it to a single-cell RNA-seq dataset to identify meaningful biological processes hidden in the data.

Gyu Hwan Park

The University of Melbourne

Gyu Hwan Park is a third-year Bachelor of Science student at the University of Melbourne, majoring in Mathematics & Statistics. He is intrigued by the intersection of modern applied Statistics and Computing in Statistical Machine Learning, Deep Learning and Bayesian Inference, particularly for their ever-increasing impacts in today’s world. With such interests, Gyu Hwan desires to develop further expertise and contribute to meaningful research in the future.

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