Please feel welcome and invited to distribute this within your departments/schools. - - ------------------- ------------------------ ------- - - PhD scholarship: Statistics and Computer Science Rating and ranking sports players and teams using Minimum Message Length [Australian Postgraduate Award (Industry): APAI Australian Research Council (ARC) Linkage Project LP100200865 Chief Investigator: David Dowe, Monash University Industry Partner: Cadability Pty Ltd] Location: Clayton School of Information Technology, Monash University, Clayton (this is a suburb of Melbourne), Australia Starting date: May 2011 or as soon as possible thereafter Title: Rating and ranking sports players and teams using Minimum Message Length Project Background: Minimum Message Length (MML) is a unifying principle in machine learning (computer science, artificial intelligence), statistics, econometrics, inductive inference and what many nowadays seem to call ``data mining''. With its origins in Bayesian information theory (Wallace & Boulton, Computer J [Oxford Univ Press], 1968), MML is now regularly published in the statistics, philosophy and econometrics literature - including (e.g.) J Royal Stat Soc (B) (1987a, 1987b, 1992), Brit J Philos Sci (2007, pp709-754), Handbook of Philos of Science - Vol. 7, Phil of Statistics (2010, pp901-982), etc. The information-theoretic underpinnings of MML not only make it a statistically invariant Bayesian form of model selection and point estimation, but they also make it readily amenable to combining the discrete and the continuous, such as (e.g.) the hybrid Bayesian networks in Comley & Dowe (2003) and Comley & Dowe (MIT Press, April 2005). [These two papers, fololowing on Dowe & Wallace (1998), are the first papers combining both discrete- and continuous-valued attributes in MML Bayesian networks.] All of this is alongside practical applications in a broad range of areas. The formal relationship between Kolmogorov complexity and MML is perhaps best described in Wallace & Dowe (1999a, Computer J, pp270-283) or in Wallace (2005, chap. 2) - although it has certainly been discussed in other places. MML is especially effective when there is much noise in the data, model misspecification and/or (as in the Neyman-Scott (1948) case or the case of [single and especially multiple] latent factor analysis - e.g., IQ estimation and octane rating estimation) the amount of data is comparatively small compared to the number of parameters to be estimated. Project: The project is on ``Rating and ranking sports players and teams using Minimum Message Length''. Rating systems go back at least as far as Harkness (1949) and the better-known Elo (1961) system for rating chess players. More recent attempts have been made to refine these systems in a variety of ways. We will refine the systems further - perhaps starting with chess but certainly going much further. This includes dealing with the challenging (Neyman-Scott-like) situation where, for some players and teams, there are few games per player or few games between different groups of players. Our enhanced modelling will be for a range of games and sports - including advantages such as, e.g., first move (as in chess), home ground and location, surface (as in tennis), etc. We will apply this to rating and ranking individuals and teams. We also refine how quickly ratings can change depending upon the strength of the player. All sorts of games and sports could use such better systems for rating and ranking teams. Applicant background: Applicants should have a background - including completing at least the equivalent of an undergraduate degree - in at least one of mathematics, statistics, computer science and/or (information theory and) electrical engineering. The successful candidate will have an undergraduate degree and will be at least semi-literate in at least one of mathematics and information theory, or at least interested in both areas. Some experience in computer programming in at least one programming language is highly desirable. Applicants should also be able to write computer programs - preferably in a variation of (e.g.) C or Java. If applicants consider themselves not to be strong at mathematics, then they should at least be fond of mathematics. Further reading: See C. S. Wallace (2005) www.csse.monash.edu.au/~dld/CSWallacePublications#MMLBook by the originator of MML. For even further reading, see one of (e.g.) Dowe, Gardner & Oppy (Brit J Philos Sci, 2007, pp709-754) [rated A*], or Dowe (2008a, ``Foreword re C. S. Wallace'', Computer J [Oxford Univ Press], pp523-560) [as guest editor of the Christopher Stewart WALLACE (1933-2004) memorial special issue] or Dowe (2010a, Handbook of Philosophy of Statistics, pp901-982) [e.g., at www.csse.monash.edu.au/~dld/David.Dowe.publications.html ]. [Note of interest: www.chessbase.com/newsdetail.asp?newsid=7114 .] Salary: Standard PhD scholarship (Aus$26,667p.a.) [possibly tax-free] accompanied by additional top-up. The scholarship is for the official Monash University standard duration of 3 years, although this might possibly be extended for a further 6 or possibly 12 months. Starting date: May 2011 or as soon as possible thereafter. Brief note about the sole Chief Investigator (David L. Dowe) : In 2005, a book on MML by Chris Wallace (1933-2004), the originator of MML in Wallace & Boulton (Computer J, 1968), was published posthumously. In the Wallace (2005) book, Dowe is the most cited living person in the reference list, Dowe has the most number of pages devoted to his work of any living person, Dowe is the most mentioned living person in the table of contents (sec. 4.10 and sec. 4.12.2 both mention his name in their titles) and Dowe is individually singled out for special gratitude in the preface on page vi. - - ----------------------- - - Supervisor's credentials include (e.g.) : ----------------------------------------- In Chris Wallace (1933-2004)'s posthumous ``Statistical and Inductive Inference by Minimum Message Length'' (2005), (a) I am given special mention in the preface on page vi, (b) I am the only living person mentioned in the table of contents, where my name appears twice, (c) I am the living person whose name and work are most mentioned in the index, (d) other than Chris Wallace himself, (in the reference list) I am the most cited author. Wallace & Dowe (1999a) was once the Computer J (OUP)'s most downloaded article - and currently remains as Chris Wallace's most cited co-authored work by a researcher still active in the area. Hernandez-Orallo & Dowe (2010) is currently the (A* rated) Artificial Intelligence J's most downloaded article, recently featured (5/March/2011, page 82) in The Economist magazine (to name one of 60+ pieces of media coverage). I co-authored the first papers on MML Bayesian nets which combine both discrete (multi-valued) and continuous-valued attributes. I have proved [Dowe (2008a, 2008b, 2011)] a uniqueness result about the invarian ce of log(arithm)-loss probabilistic scoring. [This follows on my publishing papers with probabilistic models and log-loss scoring since 1993 - well before many people saw the merits of doing probabilistic classification.] I have been invited to contribute a piece for the forthcoming Handbook of Philosophy of Statistics. Etc. [Please forgive the not so self-effacing section immediately above, but recent circumstances seem to render it necessary. (That said, please let me know if you can't find any of the abovementioned references or if you'd like further reading or other details.)] - - ----------------------- - - For prospective applicants: [Please understand that I expect many enquiries and applications, so the more clearly applicants express themselves and the easier they make my life :-) , the higher their probability of getting due attention.] Enquiries: Contact David dot Dowe arroba infotech dot monash.edu dot au www.csse.monash.edu.au/~dld/David.Dowe.publications.html (www.Solomonoff85thMemorial.monash.edu.au) with clear e-mail subject line and contents. Applications: Cover letter and application - as above - including academic transcripts, proof (if appropriate) of English language quality (e.g., IELTS or TOEFL) [as this is a Monash University requirement], addressing selection criteria, and including c.v./resume'. For information about English language requirements [in ``Information Technology''], see www.mrgs.monash.edu.au/futurestudents/eligibility/langprof.html . If applying, please send application once - in one e-mail - and please include every relevant file as a separate attachment. Again, send to David dot Dowe arroba InfoTech dot monash.edu dot au www.csse.monash.edu.au/~dld/David.Dowe.publications.html (www.Solomonoff85thMemorial.monash.edu.au) with clear e-mail subject line and contents.