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.

You may be interested in

Dean Noble

Dean Noble

Nonlinear Galerkin Methods
Liam Blake

Liam Blake

Developing Measures of User Behaviour in Online Social Networks
Aidan Brohm

Aidan Brohm

Escher Configurations on Triangles
Cameron Shaw-Carmody

Cameron Shaw-Carmody

Experimental Number Theory with Sage
Contact Us

We're not around right now. But you can send us an email and we'll get back to you, asap.

Not readable? Change text.