# Reading group

**Usual day and time:** currently not running

**Place:** TBA

## 2017/2018

14th, 28th Sep, 5th, 12th Oct -** "ABC samplers"**

https://arxiv.org/abs/1802.09650

This paper is a chapter from Handbook of Approximate Bayesian Computation (2018).

17th, 23rd Aug & 7 Sep - **"Stein Points"**

https://arxiv.org/abs/1803.10161

Related material:

Manuel and Peter Jan's paper on their variational mapping particle filter https://arxiv.org/pdf/1805.11380.pdf

Liu and Wang's paper: https://arxiv.org/pdf/1608.04471.pdf

6th July - **"Auxiliary gradient-based sampling algorithms"**

ttps://arxiv.org/pdf/1610.09641.pdf

1st & 15th June - **“Scaling analysis of delayed rejection MCMC methods”**

http://www.dms.umontreal.ca/~bedard/mcap608.pdf

11th, 18th & 24th May - **“Inference in generative models using the Wasserstein distance”**

https://arxiv.org/abs/1701.05146

20th, 27th Apr & 4th May - **"Accelerating Markov Chain Monte Carlo with Active Subspaces"**

http://epubs.siam.org/doi/pdf/10.1137/15M1042127

23rd Mar -** “Bayesian Inference on Principal Component Analysis using Reversible Jump Markov Chain Monte Carlo”**

https://pdfs.semanticscholar.org/3a68/295b2a34139a62695540bd3852dabca85060.pdf

16th Feb & 9th, 16th Mar** **- **“Discontinuous Hamiltonian Monte Carlo for sampling discrete parameters”**

https://arxiv.org/abs/1705.08510

2nd & 9th Feb - **“The Stan Math Library: Reverse-Mode Automatic Differentiation in C++”**

http://arxiv.org/abs/1509.07164

12th, 19th & 26th Jan - **"Bayesian Synthetic Likelihood"**

http://www.tandfonline.com/doi/full/10.1080/10618600.2017.1302882 (UoR login required)

17th, 24th Nov, 1 & 8th Dec - **"Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models"**

http://jmlr.org/papers/volume18/15-397/15-397.pdf

20th, 27th Oct & 3rd Nov - **"Stochastic Variational Inference"**

http://www.columbia.edu/~jwp2128/Papers/HoffmanBleiWangPaisley2013.pdf

13th Oct - **"Unsupervised learning of finite mixture models"**

http://ieeexplore.ieee.org/abstract/document/990138/ (UoR login required)

http://www.lx.it.pt/~mtf/IEEE_TPAMI_2002.pdf (direct link)

29th Sep & 6th Oct - **"The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data"**

## 2016/2017

9th Jun & 7th Jul - **"Gibbs Flow for Approximate Transport with Applications to Bayesian Computation"**

http://arxiv.org/abs/1509.08787

2nd June - **"Efficient Bayesian inference for exponential random graph models by correcting the pseudo-posterior distribution"**

http://arxiv.org/pdf/1510.00934.pdf

28th Apr, 12th & 19th May 2017 - **"Sequential Monte Carlo with Highly Informative Observations"**

http://arxiv.org/abs/1405.4081

24th, 31st Mar & 7th Apr 2017 - **"Anytime Monte Carlo"**

http://arxiv.org/pdf/1612.03319.pdf

10th & 17th Mar 2017 - **"Implicit Particle Methods and Their Connection with Variational Data Assimilation"**

http://doi.org/10.1175/MWR-D-12-00145.1

3rd Mar 2017 - **"Inference of population structure using dense haplotype data"**

http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1002453

Useful: http://paintmychromosomes.com/

10th, 17th & 24th Feb 2017 - **"An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach”**

http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2011.00777.x/abstract

13th, 20th, 27th Jan & 3rd Feb 2017 - **"The Scalable Langevin Exact Algorithm: Bayesian Inference for Big Data”**

http://arxiv.org/abs/1609.03436

Related: http://arxiv.org/abs/1607.03188

2nd & 9th Dec 2016 - **"On Markov chain Monte Carlo methods for tall data”**

http://arxiv.org/abs/1505.02827

25th Nov 2016 -** "Bayesian Learning via Stochastic Gradient Langevin Dynamics”**

http://www.stats.ox.ac.uk/~teh/research/compstats/WelTeh2011a.pdf

4th, 11th & 18th Nov 2016 -** "MCMC using Hamiltonian dynamics”**

http://arxiv.org/pdf/1206.1901.pdf

Useful: http://projecteuclid.org/euclid.aoap/1027961031

28th Oct 2016 - **"Adaptive importance sampling in general mixture classes"**

## Previous years

Jun 2016 - **“Can local particle filters beat the curse of dimensionality?”**

Paper by Patrick Rebeschini and Ramon van Handel

http://arxiv.org/abs/1301.6585

May 2016 - **“Importance Sampling: Computational Complexity and Intrinsic Dimension”**

Paper by Sergios Agapiou, Omiros Papaspiliopoulos, Daniel Sanz-Alonso and Andrew M. Stuart

http://arxiv.org/abs/1511.06196

29th Apr 2016 - **Sampling methods commonly used in theoretical polymer physics**

Discussion without paper

23rd Mar 2016 - **“Exact and computationally efficient likelihood-based estimation for discretely observed diffusion processes”**

Paper by Alexandros Beskos, Omiros Papaspiliopoulos, Gareth O. Roberts and Paul Fearnhead

http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2006.00552.x/abstract

10th Feb 2016 -** “A Semi-Parametric Bayesian Approach to the Instrumental Variable Problem”**

Paper by Timothy G. Conley, Christian Hansen, Robert E. McCulloch and Peter E. Rossi

http://www.ssrn.com/abstract=917432

20th Jan 2016 - **“Markov Chain Sampling Methods for Dirichlet Process Mixture Models”**

Paper by Radford M. Neal

http://www.stat.columbia.edu/npbayes/papers/neal_sampling.pdf

Dec 2015 - **“Annealed Importance Sampling Reversible Jump MCMC Algorithms”**

Paper by Georgios Karagiannis and Christophe Andrieu

http://www.tandfonline.com/doi/abs/10.1080/10618600.2013.805651

Nov 2015 - **“Model choice using reversible jump Markov chain Monte Carlo”**

Paper by David I. Hastie and Peter J. Green

http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9574.2012.00516.x/abstract

10th Nov 2015 -** A comparison of an ensemble Kalman filter and a particle filter for DA in the barotropic vorticity equation**

Talk about work in progress without a paper

3rd Nov 2015 - **“Physiological Pharmacokinetic Analysis Using Population Modeling and Informative Prior Distributions”** (joint with the Math Bio group)

Paper by Andrew Gelman , Frederic Bois and Jiming Jiang

http://www.stat.columbia.edu/~gelman/bayescomputation/GelmanBoisJIang1996.pdf

28th Oct 2015 -** “Leave Pima Indians alone: binary regression as a benchmark for Bayesian computation”**

Paper by Nicolas Chopin and James Ridgway

http://arxiv.org/abs/1506.08640

Feb 2015 - **"Particle Markov chain Monte Carlo methods" **

Paper by Christophe Andrieu, Arnaud Doucet

and Roman Holenstein

http://www.stats.ox.ac.uk/~doucet/andrieu_doucet_holenstein_PMCMC.pdf

18th Feb 2015 -** “MCMC for doubly-intractable distributions”**

Paper by Iain Murray, Zoubin Ghahramani and David J. C. MacKay

http://arxiv.org/pdf/1206.6848.pdf

Jan 2015 - **"MCMC Methods for Functions: Modifying Old Algorithms to Make Them Faster"**

Paper by Simon L. Cotter, Gareth. O. Roberts, Andrew. M. Stuart and David White

http://arxiv.org/abs/1202.0709

26th Nov 2014 -** "Sequential Quasi-Monte Carlo”**

Paper by Mathieu Gerber and Nicolas Chopin

http://arxiv.org/abs/1402.4039

19th Nov 2014 -** Equivalent weights particle filter for very high dimensional problems**

Two papers on this topic by Melanie Ades and Peter J. van Leeuwen

http://onlinelibrary.wiley.com/doi/10.1002/qj.1995/full

http://onlinelibrary.wiley.com/doi/10.1002/qj.2370/full

29th Oct 2014 - **"Asynchronous Anytime Sequential Monte Carlo"**

Paper by Brooks Paige, Frank Wood, Arnaud Doucet and Yee Whye Teh

http://arxiv.org/abs/1407.2864/

29th Apr 2014 -** "Statistical inference for noisy nonlinear ecological dynamic systems"**

Paper by Simon N. Wood

http://www.nature.com/nature/journal/v466/n7310/abs/nature09319.html

6th Feb 2014 -** "Sequential Monte Carlo samplers"**

Paper by Pierre Del Moral, Arnaud Doucet and Ajay Jasra

http://www.stats.ox.ac.uk/~doucet/delmoral_doucet_jasra_sequentialmontecarlosamplersJRSSB.pdf

3rd Dec 2013 -** "A Tutorial on Particle Filtering and Smoothing: Fifteen years later"**

Paper by Arnaud Doucet and Adam M. Johansen

http://www.cs.ubc.ca/~arnaud/doucet_johansen_tutorialPF.pdf

24th Nov 2013 -** "A Survey of Implicit Particle Filters for Data Assimilation"**

Paper by Alexandre J. Chorin, Matthias Morzfeld and Xuemin Tu

http://math.berkeley.edu/~chorin/CMT12.pdf

24th Oct 2013 -** "Efficient learning in ABC algorithms"**

Paper by Mohammed Sedki, Pierre Pudlo, Jean-Michel Marin, Christian P. Robert and Jean-Marie Cornuet

http://arxiv.org/abs/1210.1388