We meet fortnightly on Fridays at 14:00 BST. If you are interested in giving a talk, email Domenic (ddifrancesco@turing.ac.uk) or Lawrence (lbull@turing.ac.uk).

*Past organisers*

Connor Duffin (October 2021 - July 2022)

Omer Deniz Akyildiz (April 2021 - September 2021)

Ieva Kazlauskaite (October 2020 - March 2021)

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

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

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:

Alessandro will present the following three papers:

- 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
- 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.
- Hyvärinen, Aapo. “Estimation of Non-Normalized Statistical Models by Score Matching.” Journal of Machine Learning Research 6, no. Apr (2005): 695-709. JMLR link

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

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

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.

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.

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.

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.

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.