We meet every week on Friday at 15:00.

Lent Term 2021 Schedule

2021-06-11 - TBA - Ben Boys
2021-06-04 - TBA - Ben Boys
2021-05-07 - TBA - Kangrui Wang
2021-04-30 - Continuous Time Limit of Gradient Algorithms - Justin
2021-04-23 - Statistical finite element methods for nonlinear PDEs - Connor


In this term, we are following the “Applied Stochastic Differential Equations” book by Särkkä and Solin.

2021-04-16 - Ch 12 Stochastic Differential Equations in Machine Learning - Alessandro
2021-03-26 - Ch 11 Parameter Estimation in SDE Models - Alex
2021-03-19 - Ch 10 Filtering and Smoothing Theory - Connor
2021-03-12 - Ch 9 Approximation of Nonlinear SDEs - Hussein
2021-03-05 - Ch 8 Numerical Simulation of SDEs - Jan (notes, notebook)
2021-02-26 - An overview of nonasymptotic analysis for the stochastic gradient Markov chain Monte Carlo - Deniz
2021-02-19 - Ch 7 Useful Theorems and Formulas for SDEs - Justin (notes)
2021-02-12 - Ch 6 Statistics of Linear Stochastic Differential Equations - Ben (notes)
2021-01-29 - Ch 5 Probability Distributions and Statistics of SDEs - Yannis (notes)
2021-01-22 Ch 4 Itô Calculus and Stochastic Differential Equations (notes)

Michaelmas Term 2020 Schedule

In this term, we will focus on the topic of approximate Bayesian computation and Stein methods.

2020-12-11 Stochastic Variational Gradient Descent (notes)

Jan will present the Stochastic Variational Gradient Descent paper.

The main reference is: Liu, Q. and D. Wang (2016). Stein variational Gradient descent: a general purpose Bayesian inference algorithm. Proceedings of the 30th International Conference on Neural Information Processing Systems. Barcelona, Spain, Curran Associates Inc.: 2378–2386. arXiv link

Other useful resources are:

2020-12-04 Score matching, Kernel Stein Discrepancy (notes)

Alessandro will present the following three papers:

  1. Barp, Alessandro, Francois-Xavier Briol, Andrew Duncan, Mark Girolami, and Lester Mackey. “Minimum Stein Discrepancy Estimators.” Paper presented at the Advances in Neural Information Processing Systems, 2019. NeurIPS link
  2. Dawid, Alexander Philip, and Monica Musio. “Theory and Applications of Proper Scoring Rules.” METRON 72, no. 2 (2014): 169-83. DOI link. research gate link.
  3. Hyvärinen, Aapo. “Estimation of Non-Normalized Statistical Models by Score Matching.” Journal of Machine Learning Research 6, no. Apr (2005): 695-709. JMLR link
2020-11-27 Introduction to Kernel Methods II with the focus on MMD

We will discuss the second part of the MLSS 2020 lecture on Kernel Methods by Arthur Gretton, with the focus on MMD.

2020-11-20 Introduction to Kernel Methods I (notes)

Justin will present Arthur Gretton’s introductory lecture into kernel methods.

2020-11-13 Sequential Monte Carlo without Likelihoods (notes)

Andrius will present the following paper:

Sisson, S. A., Y. Fan, and M. M. Tanaka. “Sequential Monte Carlo without Likelihoods.” Proceedings of the National Academy of Sciences 104, no. 6 (February 6, 2007): 1760–65. https://doi.org/10.1073/pnas.0607208104.

2020-11-06 ABC Methods for Climate Modelling (notes)

Yannis will present present chapter 19 of the Handbook of Approximate Bayesian Computation.

Sisson, S. A., Y. Fan, and M. A. Beaumont, eds. 2019. Handbook of Approximate Bayesian Computation. Boca Raton: CRC Press, Taylor and Francis Group.

2020-10-30 Summary Statistics (notes)

Alex will present chapter 5 of the Handbook of Approximate Bayesian Computation.

Sisson, S. A., Y. Fan, and M. A. Beaumont, eds. 2019. Handbook of Approximate Bayesian Computation. Boca Raton: CRC Press, Taylor and Francis Group.

2020-10-23 Statistical Inference for Noisy Nonlinear Ecological Dynamic Systems (notes)

Connor will present the following paper by Simon Wood.

Wood, Simon N. 2010. ‘Statistical Inference for Noisy Nonlinear Ecological Dynamic Systems’. Nature 466 (7310): 1102–4. https://doi.org/10.1038/nature09319.

2020-10-16 Introduction to Approximate Bayesian Computation (notes)

Ieva will present the introductory chapter of the Handbook of Approximate Bayesian Computation.

Sisson, S. A., Y. Fan, and M. A. Beaumont, eds. 2019. Handbook of Approximate Bayesian Computation. Boca Raton: CRC Press, Taylor and Francis Group.