Jordan Franks
About me
I am a mathematician. I did my PhD about numerical simulation methods known as Monte Carlo methods which can be useful for statistical Bayesian inference.
Journal articles
N. Chada, F, A. Jasra, K. Law and M. Vihola. Unbiased inference for discretely observed hidden Markov model diffusions. J. Uncertainty Quantif., Vol. 9(2), 2021. [arXiv:1807.10259]
M. Vihola, J. Helske and F. Importance sampling type estimators based on approximate marginal MCMC. Scand. J. Statist., Vol. 47(4), 2020. [arXiv:1609.02541]
F and M. Vihola. Importance sampling correction versus standard averages of reversible MCMCs in terms of the asymptotic variance. Stochastic Process. Appl., Vol. 130(10), 2020. [arXiv:1706.09873]
M. Vihola and F. On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction. Biometrika, Vol. 107(2), 2020. [arXiv:1902.00412]
F and A. Valette. On 1-cocycles induced by a positive definite function on a locally compact abelian group. Ann. Math. Blaise Pascal, Vol. 21(1), 2014. [arXiv:1303.4185]
Theses
Markov chain Monte Carlo importance samplers for Bayesian models with intractable likelihoods. PhD thesis, University of Jyväskylä, May 2019. Introduction
Conductance and mixing time bounds for 1-dimensional spin models: Toom interface, Metropolis, and simple exclusion. Master's thesis, University of Bonn, March 2016.
Degrees
PhD, Statistics, University of Jyväskylä, Jyväskylä, Finland, 2019
MS, Mathematics, University of Bonn, Bonn, Germany, 2016
BA, Mathematics (major), Computational and Applied Math (minor), Rice University, Houston, USA, 2012
AS, Physics, Mt. Hood Community College, Portland, USA, 2008
Contact information
Email: franks AT iki DOT fi