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Research Highlight
The Bayesian paradigm for statistical inference uses expert knowledge, formulated in terms of probability distributions of unknown parameters of interest. These distributions, called prior distributions, are combined with data to provide new information about parameters, via new parameter distributions called posterior distributions. One research theme centers on devising new Bayesian methodologies, i.e., new statistical models with which Bayesian inferences can provide particular scientific insight. Quantifying the statistical properties of such methods and contrasting with non-Bayesian alternatives is an active area of research. Bayesian methods can lead to computational challenges, and another research theme centers on efficient computation of Bayesian solutions. The development of computational techniques for determining posterior distributions, such as Monte Carlo methods, is a rich area of research activity, with particular emphasis on Markov Chain Monte Carlo methods and sequential Monte Carlo methods.
Events
News
Applied Statistics and Data Science Group at UBC Statistics is pleased to announce the launch of the webinar series on Data analysis: Practical application of Linear Mixed Effects Models. ...
The Department of Statistics is soliciting nominations for the Department of Statistics Award in Data Science, an award to recognize the importance of developing and applying tools to answer important questions through the analysis of data....
The Statistics Department offers several types of free statistical consultation, via STAT 450/550, SOS and STAT 551 as follows:
- STAT 450/550: Term 2 only, open to anyone, via students in STAT 450 mentored by faculty; ...
UBC Statistics Professor Jiahua Chen has been named a new Fellow of the Royal Society of Canada (RSC), Canada’s highest academic honour. Founded in 1882, the Royal Society of Canada (RSC)...