Primary supervisor
David DoweResearch area
Machine Learning and Deep LearningThere are many approaches to hypothesis testing. The well-known approach of p-values has been drawn into question and even controversy in more recent years, even though criticisms reportedly date back at least as far as 1954 (Dowe, 2008a, sec. 1, pp549-550).
Discussion of how to do this using the Bayesian information-theoretic minimum message length (MML) approach (Wallace and Boulton, 1968; Wallace and Dowe, 1999a; Wallace, 2005) are given in Dowe (2008a, section 0.2.5, page 539, and section 0.2.2, page 528), and Dowe (2011, pages 919 and 964).
References:
D. L. Dowe (2008a), "Foreword re C. S. Wallace", Computer Journal, Vol. 51, No. 5 (Sept. 2008) [Christopher Stewart WALLACE (1933-2004) memorial special issue [and front cover and back cover]], pp523-560 (and here). www.doi.org: 10.1093/comjnl/bxm117
D. L. Dowe (2011a), "MML, hybrid Bayesian network graphical models, statistical consistency, invariance and uniqueness", Handbook of the Philosophy of Science - (HPS Volume 7) Philosophy of Statistics, P.S. Bandyopadhyay and M.R. Forster (eds.), Elsevier, [ISBN: 978-0-444-51862-0 {ISBN 10: 0-444-51542-9 / ISBN 13: 978-0-444-51862-0}], pp901-982, 1/June/2011
L Held (2020), "A new standard for the analysis and design of replication studies", J Royal Statist Soc. (A), vol. 183, no. 2, pp431-448
K Rice, T Bonnett and C Krakauer (2020), "Knowing the signs: a direct and generalizable motivation of two‐sided tests", J Royal Statist Soc. (A), vol. 183, no. 2, pp411-430
Wallace, C.S. (2005), ``Statistical and Inductive Inference by Minimum Message Length'', Springer (Link to the preface [and p vi, also here])
Wallace, C.S. and D.M. Boulton (1968), ``An information measure for classification'', Computer Journal, Vol 11, No 2, August 1968, pp 185-194
Wallace, C.S. and D.L. Dowe (1999a). Minimum Message Length and Kolmogorov Complexity, Computer Journal (special issue on Kolmogorov complexity), Vol. 42, No. 4, pp270-283
Wallace, C.S. and P.R. Freeman (1987). Estimation and inference by compact coding. J. Royal Statist. Soc. B, 49, 240–252.
Required knowledge
Important is a knowledge of at least one of mathematics, statistics and/or machine learning principles - including at least interest in probability theory - with at least one of these at least to the level of an undergraduate degree. Candidates should also have a strong computer science background with good programming skills.
