Misc

Statistical Models for Dependent Data
Dependency among observations can arise from a multitude of sources, including spatial or temporal correlation, grouped, clustered or …
Simon Fontaine
June, 2024 PhD thesis, University of Michigan Misc
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Missing Value Imputation in Relational Data using Variational Inference
In real-world networks, node attributes are often only partially observed, necessitating imputation to support analysis or enable …
Simon Fontaine, Jian Kang, Ji Zhu
January, 2024 Under revision at JCGS Misc
Code Project
An adaptive multiple try Metropolis algorithm
Markov chain Monte Carlo (MCMC) methods, specifically samplers based on random walks,often have difficulty handling target …
Simon Fontaine, Mylène Bédard
August, 2022 Bernoulli Misc
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Missing Value Imputation of Network Node Attributes
User profiles in social networks are notoriously incomplete due to self-reporting; predicting or imputing these missing values is often …
Aug 10, 2021 10:00 AM Online
Simon Fontaine, Ji Zhu
Slides
A Unified Approach to Sparse Tweedie Modeling of Multisource Insurance Claim Data
Actuarial practitioners now have access to multiple sources of insurance data corresponding to various situations: multiple business …
Simon Fontaine, Yi Yang, Wei Qian, Bowen Gu, Bo Fan
January, 2020 Technometrics Misc
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Online Skill Rating Algorithms
Skill rating algorithms are now ubiquitous in online competitive video games and are used not only to accurately compare players’ …
Aug 1, 2019 8:30 AM Denver, CO, USA
Nicolas Grenon-Godbout, Jonathan Dumas, Simon Fontaine, Gabrielle Rit, Timothy Park
MCMC adaptatifs à essais multiples
This memoir aims at introducing adaptation within the Multiple-Try Metropolis (MTM) algorithms which are a special case of the Markov …
Simon Fontaine
January, 2019 Master’s thesis, University of Montreal Misc
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