BS Missouri MSc Stan DPhil Waikato
- Room No: G3.31
- Telephone: +64 7 838 4466 ext 8334
- Extension: 8334
- Facsimile: +64 7 838 4155
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Bill has 30 years teaching experience at the University of Waikato and is currently a Senior Lecturer in the Department of Statistics. After completing his graduate studies in mathematics at the University of Missouri in 1965, Bill then carried on with mathematical statistics at Stanford University, California while working at Lockheed Missile & Space Co. as an Associate Engineer and continuing on to the position of Dynamics Engineer by 1968. He later moved to New Zealand where he tutored part-time in mathematics and statistics at the Waikato Polytechnic in Hamilton, going on to teach at secondary level at Hamilton Boys High School until joining the University staff in 1975.
My hobbies include dinosaurs and volcanoes.
I was fortunate to be able to capture Mt Ruapehu during the eruptions in 1996 and won a local photography award with this photograph.
Bayesian statistics, MCMC methods, Recursive estimation techniques; multiprocess dynamic time series models; forecasting and control.
I am interested in Bayesian statistics and Bayesian methods applied to time series analysis. With the advent of Markov chain Monte Carlo sampling based methods, Bayesian methods can now be applied to a much wider class of models than the restricted class that is, analytically tractible. Removing this impediment will lead to a much greater use of Bayesian statistics in the future, allowing their known theoretical advantages to be utilized.
One of the main reasons that frequentist methods dominate current statistical practice is the lack of exposure to Bayesian ideas in introductory statistics courses. I have written a book, Introduction to Bayesian Statistics, to introduce students to this approach which is widely used all over the world, as well as being the text for STAT226B at Waikato. This year I am introducing a new course, STAT326B 'Computational Bayesian Statistics', and am currently engaged in writing another book based on this course.
- Man Jit Ching (2000)Developing statistical methods for
analysis of population health patterns.(PhD)
- Samuel Manda (1999)Survival and hazard model for children under five in Malawi (PhD)
- Khangelani Zuma (2004)The statistical models of migration and the spread of HIV and other sexually transmitted diseases (PhD)
Bolstad, W. M. (2010) Understanding Computational Bayesian Statistics John Wiley & Sons, New York
A hands-on introduction to computational statistics from a Bayesian point of view. Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective. Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula givings its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustrated on common statistical models, including the multiple linear regression model, the hierarchical mean model, the logistic regression model and the proportional hazards model.