Bayesian Reading Group
University of Reading
University of Reading
Usual day and time: Friday @ 1pm.
Place: Room 108 in Maths (for the rest of the Autumn term).
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"
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"
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