The contents of this website are based on the PhD thesis entitled Regression Modelling with Priors using Fisher Information Covariance Kernels (I-priors) by Haziq Jamil. It is intended as an accompaniment to the research poster of the same title.
Haziq Jamil is a research student in Statistics at the London School of Economics and Political Science. His interests are in statistical methodology and computation, with an emphasis on applications towards the social sciences.
Haziq graduated with first class honours from The University of Warwick in 2010, where he read Mathematics, Operational Research, Statistics and Economics. He completed an MSc in Statistics with distinction at the London School of Economics and Political Science in 2014. He is expected to complete his PhD from the same university in 2018.
Previously, Haziq worked as a research officer at the Centre of Science and Technology, Research and Development at the Ministry of Defence, Brunei, where he provided scientific decision support for strategic acquisition projects.
- Source - GitHub
- PhD Poster - PDF
- R/iprior package - CRAN, GitHub
- R/iprobit package - GitHub
- R/ipriorBVS package - GitHub
- HMC explainer - Shiny Apps 1, 2
- Example of variational inference - link
- Bergsma, W. (2017, July). Regression and classification with I-priors. Manuscript in preparation. arXiv: 1707.00274.
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- Neal, R. M. (2011). MCMC using Hamiltonian dynamics. Handbook of Markov Chain Monte Carlo, 2(11). Chapman & Hall/CRC Press. arXiv: 1206.1901v1.
- Ong, C. S., Mary, X., Canu, S., & Smola, A. J. (2004, July). Learning with non-positive kernels. In Proceedings of the Twenty-first International Conference on Machine Learning (p. 81). ACM. DOI: 10.1145/1015330.1015443 .
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- Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. ISBN: 978-0-387-31073-2.
- Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017). Variational Inference: A Review for Statisticians. Journal of the American Statistical Association, to appear. arXiv: 1601.00670.
- Girolami, M., & Rogers, S. (2006). Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors. Neural Computation, 18(8), 1790-1817. DOI: 10.1162/neco.2006.18.8.1790.
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- Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models. Chapman & Hall/CRC. ISBN: 978-1-58488-000-4.
Copyright © 2014-2017 by Haziq Jamil. All rights reserved. The contents of this website or any portion thereof may not be reproduced or used in any manner whatsoever without the express written permission of the author.