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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
Statistics PhD candidates Kenny Chiu and Naitong Chen were honoured with the...
Congratulations, to the 2024 awardee, Farhan Samir! Farhan is in his fourth year of a Ph.D. in computational linguistics. He holds an NSERC Postgraduate Scholarship and an NSERC Postdoctoral Fellowship for his future postdoc studies. His...
The CANSSI Prairies Regional Centre organized the CANSSI Prairies Workshop Series in Data Science in 2023, offering an excellent opportunity for individuals to enhance their...
We were very happy to read the Government of Canada’s recent announcement of new and renewed Canada Research Chairs (CRCs), especially because one of our professors, Natalia Nolde,...