Talks

2024

April 11th: A53 (PSC)
François-Xavier Briol
Robust and conjugate Gaussian process regression [arxiv]
March 21st: STOR-i boardroom
Leandro Marcolino
Identifying Adversaries in Ad-hoc Domains Using Q-valued Bayesian Estimations [AAMAS]
March 14th: A53 (PSC)
Theo Papamarkou
Aspects of sampling-based inference for Bayesian neural networks
March 7th: STOR-i boardroom
Tamas Papp
Simulating the independence sampler parallel-in-time [work in progress]
February 22nd: STOR-i boardroom
Francesco Barile
Flexible modeling of heterogeneous populations of networks: a Bayesian nonparametric approach [work in progress]
February 15th: Lecture Theatre (PSC)
Kamélia Daudel
Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics [JMLR]
February 8th: STOR-i boardroom
Estevão Prado
Accounting for shared covariates in semi-parametric Bayesian additive regression trees [arxiv]
February 1st: STOR-i boardroom
Lorenzo Rimella
A State-Space Perspective on Modelling and Inference for Online Skill Rating [arxiv]

2023

December 14th: A54 (PSC)
Chris Jewell
Data Augmentation MCMC on epidemic models [work in progress]
December 7th: A54 (PSC)
Marina Riabiz
Optimal Thinning of MCMC Output [JRSSB]
November 30th: A53 (PSC)
Lorenzo Rimella
Simulation Based Composite Likelihood [arxiv]
November 23rd: A53 (PSC)
Andy Wang
Comparison theorems for Hybrid Slice Sampling [work in progress]
November 16th: STOR-i boardroom
Chris Sherlock
Ensemble Kalman filter
November 9th: STOR-i boardroom
Chris Nemeth
Bayesian Flow Networks [arXiv]
November 2nd: A54 (PSC)
Aretha Teckentrup
Gaussian processes, inverse problems and Markov chain Monte Carlo
October 26th: STOR-i boardroom
Sam Holdstock
Improved inference for stochastic kinetic models with small observation error via partial Rao-Blackwellisation [work in progress]
October 19th: STOR-i boardroom
Estevão Prado
Metropolis-Hastings with fast, flexible sub-sampling [work in progress]
October 12th: STOR-i boardroom
Alberto Cabezas
Composable Inference in BlackJAX
June 29th: STOR-i boardroom
Tamas Papp
Introduction to diffusion generative models
June 22nd: STOR-i boardroom
Chris Sherlock
Fast return-level estimates for flood insurance via an improved Bennett inequality for random variables with differing upper bounds [Ongoing work]
June 15th: STOR-i boardroom
Alice Corbella (University of Warwick)
The Lifebelt Particle Filter for robust estimation from low-valued count data [arXiv]
June 8th: STOR-i boardroom
Francesca Panero (London School of Economics)
Modelling sparse networks with Bayesian nonparametrics [Ongoing work]
May 18th: STOR-i boardroom
Lorenzo Rimella
Localised filtering algorithm: the BPF and the Graph Filter [BPF] [Graph Filter]
May 11th: STOR-i boardroom
Francesca Crucinio (ENSAE)
Divide-and-Conquer SMC with applications to high dimensional filtering [Statistica Sinica]
April 27th: STOR-i boardroom
Paul Fearnhead
Automatic Differentiation of Programs with Discrete Randomness [NeurIPS]
March 2nd: STOR-i boardroom
Chris Sherlock
KSD for dummies
February 23rd: Lecture theatre PSC A54
Victor Elvira (University of Edinburgh)
State-Space Models as Graphs [arXiv]
February 16th: Lecture theatre PSC A54
Sam Livingstone (University College London)
Pre-conditioning in Markov chain Monte Carlo [Ongoing work]
January 26th: STOR-i Hub
Estevao Batista Do Prado
Bayesian additive regression trees (BART) [Ann. Appl. Stat. ], [JASA]
January 19th: STOR-i Hub
Alberto Cabezas Gonzalez
Stereographic Markov Chain Monte Carlo [arXiv]
January 12th: Lecture theatre PSC A54
Alexander Terenin (University of Cambridge)
Pathwise Conditioning and Non-Euclidean Gaussian Processes [arXiv]

2022

December 15th: STOR-i Hub
Tamas Papp Coupling MCMC algorithms in high dimensions
December 8th: STOR-i Hub
Yu Luo Bayesian estimation using loss functions
November 24th: Lecture theatre PSC A54
Sam Power (University of Bristol) Explicit convergence bounds for Metropolis Markov chains: isoperimetry, spectral gaps and profiles
November 17th: Lecture theatre PSC A54
Mauro Camara Escudero (University of Bristol) Approximate Manifold Sampling
November 3rd: Lecture theatre PSC A54
Alexandros Beskos (UCL) Manifold Markov chain Monte Carlo methods for Bayesian inference in diffusion models
October 27th: Lecture theatre PSC A54
Jure Vogrinc (Warwick university) The Barker proposal: Combining robustness and efficiency in gradient-based MCMC
October 20th: STOR-i Hub
Paul Fearnhead Martingale posterior distributions
October 6th Lecture theatre PSC A54
Michael Whitehouse (University of Bristol) Consistent and fast inference in compartmental models of epidemics using PAL
September 29th STOR-i Hub
Chris Sherlock Comparison of Markov chains via weak Poincaré inequalities with application to pseudo-marginal MCMC
September 22nd STOR-i Hub
Lorenzo Rimella Inference in Stochastic Epidemic Models via Multinomial Approximations
September 15th STOR-i Hub
Chris Nemeth Metropolis–Hastings via Classification
June 23rd STOR-i Hub
Paul Fearnhead Non-Reversible Parallel Tempering: a Scalable Highly Parallel MCMC Scheme
June 9th STOR-i Hub
Augustin Chevallier Continuously-Tempered PDMP samplers
May 26th STOR-i Hub
Chris Sherlock Scalable Importance Tempering and Bayesian Variable Selection
May 5th STOR-i Hub
Steffen Grünewälder Compressed Empirical Measures (in finite dimensions)
March 31st STOR-i Hub
Louis Sharrock (University of Bristol) Parameter Estimation for the McKean-Vlasov Stochastic Differential Equation
March 24th STOR-i Hub
Alberto Cabezas Gonzalez Elliptical slice sampling
March 17th STOR-i Hub
Augustin Chevallier Slice sampling & PDMP
March 3rd STOR-i Hub
Paul Fearnhead Boost your favorite MCMC sampler using Kac’s theorem: the Kick-Kac teleportation algorithm- Part 2
February 24th STOR-i Hub
Paul Fearnhead Boost your favorite MCMC sampler using Kac’s theorem: the Kick-Kac teleportation algorithm- Part 1
February 17th STOR-i Hub
Lionel Riou-Durand (Warwick university) Metropolis Adjusted Underdamped Langevin Trajectories: a robust alternative to Hamiltonian Monte-Carlo
Februrary 3th MS Teams
Chris Sherlock Statistical scalability and approximate inference in distributed computing environments
January 27th MS Teams
Lorenzo Rimella The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
January 20th MS Teams
Augustin Chevallier Non-reversible guided Metropolis kernel
January 13th MS Teams
Szymon Urbas The Apogee to Apogee Path Sampler

2021

December 16th MS Teams
NeurIPS papers discussion
December 9th MS Teams
Chris Sherlock Metropolis-Hastings with Averaged Acceptance Ratios
December 2nd MS Teams
Chris Nemeth Waste-free sequential Monte Carlo
November 25th MS Teams
Sam Power (University of Bristol) Double Control Variates for Gradient Estimation in Discrete Latent Variable Models
November 18th MS Teams
Chris Nemeth How do you tune MCMC algorithms?
November 4th MS Teams
Augustin Chevallier Approximations of Piecewise Deterministic Markov Processes and their convergence properties
October 28th MS Teams
Paul Fearnhead Multilevel Linear Models, Gibbs Samplers and Multigrid Decompositions
October 14th MS Teams
Tamas Papp Estimating Markov chain convergence with empirical Wasserstein distance bounds
June 22nd MS Teams
Gael Martin (Monash University) landmark papers: Bayesian computation from 1763 to the 21st Century
June 10th MS Teams
Phyllis Ju (Harvard university) Sequential Monte Carlo algorithms for agent-based models of disease transmission
May 27th MS Teams
Christian P. Robert (Université Paris-Dauphine) landmark papers: Harold Jeffreys’s Theory of Probability Revisited
May 13th MS Teams
Lorenzo Rimella Dynamic Bayesian Neural Networks
April 29th MS Teams
Clement Lee landmark papers: The Gelman-Rubin statistic: old and new
April 15th MS Teams
George Bolt MCMC Sampling and Posterior Inference for a New Metric-Based Network Model
March 25th MS Teams
Jeremie Coullon landmark papers: the Metropolis sampler (1953)
March 4th MS Teams
Chris Sherlock Differentiable Particle Filtering via Entropy-Regularized Optimal Transport

2020

Thursday 19th November MS Teams
Liam Hodgkinson Stein kernels
Thursday 3rd March 3-4pm PSC Fylde B35
Chris Nemeth Deep generative modelling: autoencoders, VAEs, GANs…. and all that jazz! Part 22
Thursday 27th February 3-4pm PSC Lab 2
Chris Nemeth Deep generative modelling: autoencoders, VAEs, GANs…. and all that jazz!
Thursday 13th February 3-4pm PSC A54
Leah South The kernel Stein discrepancy

2019

December 5th 3-4pm PSC A54
Paul Fearnhead Zig Zag Sampler
November 7th 3-4pm PSC A54
Jeremias Knoblauch Generalized variational inference [Abstract] | [Slides]
November 24th 3-4pm PSC A54
Francois-Xavier Briol Statistical Inference for Generative Models with Maximum Mean Discrepancy [Abstract]
October 9th 1.30pm-2.30pm PSC A54
Magnus Rattray Using Gaussian processes to infer pseudotime and branching from single-cell data. [Abstract]
September 20th 3pm-4pm PSC A54
Sam Livingstone On the robustness of gradient-based MCMC algorithms. [Abstract]

August 1st 3pm-4pm STOR-i Hub
Leah South Variance reduction in MCMC.
June 13th 3pm-4pm PSC A54
Clement Lee Clustering approach and MCMC practicalities of stochastic block models.[Abstract]
May 9th 3pm-4pm PSC A54
Nick Tawn The Annealed Leap Point Sampler (ALPS) for multimodal target distributions.[Abstract]
March 28th, 3pm-4pm STOR-i Hub
Callum Vyner An Introduction to Divide-and-Conquer MCMC.
February 28th, 3pm-4pm PSC A54
Matthew Ludkin Hug ‘N’ Hop: Explicit, non-reversible, contour-hugging MCMC.
February 14th, 3pm-4pm STOR-i Boardroom
Henry Moss An Intro to Information-Driven Bayesian Optimisation

2018

December 13th, 3pm-4pm PSC A54
Arnaud Doucet On discrete-time piecewise-deterministic MCMC schemes
December 5th, 3pm-4pm PSC A54
Louis Aslett Privacy and Security in Bayesian Inference
November 15th, 3pm-4pm PSC A54
Chris Sherlock The Minimal Extended Statespace Algorithm for exact inference on Markov jump processes

2017

Decmber 7th, 3pm-4pm PSC A54
Gareth Ridall Sequential Bayesian estimation and model selection [Abstract]
November 29th, 11am-12pm PSC A54
Chris Nemeth Pseudo-extended MCMC [Abstract]
November 9th, 3pm-4pm PSC A54
Luke Kelly Lateral trait transfer in phylogenetic inference [Abstract]
November 1st, 2pm-3pm PSC A54
Yee Whye Teh On Bayesian Deep Learning and Deep Bayesian Learning [Abstract]
May 18th, 2pm-4pm PSC A54
Chris Sherlock Asymptotic variance and geometric convergence of MCMC: variational representations [Abstract]
January 26th, 2pm PSC A54
Chris Sherlock Delayed-acceptance MCMC with examples: advantages and pitfalls and how to avoid the latter [Abstract]

2016

December 6th, 2pm PSC A54
Jack Baker An overview of Bayesian non-parametrics.
November 11th, 2pm PSC A54
Wentao Li Improved Convergence of Regression Adjusted Approximate Bayesian Computation.
October 20th, 2pm PSC Lab2
Paul Fearnhead The Scalable Langevin Exact Algorithm: Bayesian Inference for Big Data.
July 2nd, 2pm B49
Adam Johansen The iterated auxiliary particle filter.
May 19th, 2pm PSC Lab2
Chris Sherlock Pseudo-marginal MCMC using averages of unbiased estimators.
May 9th, 2pm PSC LT
Joris Bierkens (Warwick University) Super-efficient sampling using Zig Zag Monte Carlo.
April 14th, 2pm PSC Lab1
Paul Fearnhead Research opportunities with MCMC and Big Data.
March 17th, 2pm PSC LT
Peter Neal Optimal scaling of the independence sampler.
February 25th, 2pm PSC LT
Paul Fearnhead Continuous-Time Importance Sampling (and MCMC).
February 18th, 2pm PSC LT
Borja de Balle Pigem Differentially Private Policy Evaluation.

2015

December 10th, 2pm PSC LT
Jack Baker STAN
November 26th, 2pm PSC Lab2
Paul Fearnhead Discussion of “The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method” [arXiv] and of “A Markov Jump Process for More Efficient Hamiltonian Monte Carlo” [arXiv].
October 15th, 2pm PSC LT.
James Hensman. Variational inference in Gaussian process models.
May 19th, 12pm PSC LT.
Alexandre Thiery (National University of Singapore). Asymptotic Analysis of Random-Walk Metropolis on Ridged Densities.
April 28th, 1pm PSC LT.
Chris Sherlock. Delayed acceptance particle marginal random walk Metropolis algorithms and their optimisation.
March 5th, 2pm PSC LT.
Chris Nemeth. Bayesian Inference for Big Data: Current and Future Directions.

2014

Decmber 18th, 2pm PSC LT.
Wentao Li. Discussion of the RSS read paper: “Sequential Quasi Monte Carlo” by Mathieu Gerber and Nicolas Chopin.
November 28th, 11am PSC LT.
Chris Nemeth. Particle Metropolis adjusted Langevin algorithms.
March 11th, 2pm PSC LT.
Paul Fearnhead. Reparameterisations for Particle MCMC.
February 25th, 2pm PSC LT.
Vasileios Maroulas (University of Tennessee). Filtering, drift homotopy and target tracking.

2013

Dember 11th, 1pm PSC LT.
Dennis Prangle (Universtity of Bristol). Speeding ABC inference using early-stopping simulations.
May 9th, 2pm PSC LT.
Chris Sherlock. Properties and Optimisation of the Pseudo Marginal RWM.
April 17th, 2pm PSC LT.
Anthony Lee (University of Warwick). Particle Markov chain Monte Carlo and marginal likelihood estimation: strategies for improvement.
March 22nd
Dennis Prangle. Likelihood-free parameter estimation for state space models
February 20th
Joe Mellor (University of Manchester). Thompson Sampling in Switching Environments with Bayesian Online Change Point Detection

2012

June 21st, 12.00pm (A54 Lecture Theatre)
Classification in Dynamic Streaming Environments - Nicos Pavlidis (Dept of Management Science, Lancaster University).
June 6th, 12.00pm (A54 Lecture Theatre)
Hamiltonian Monte Carlo: Beyond Kinetic Energy - Paul Fearnhead.
May 22nd, 12.00pm (A54 Lecture Theatre)
Metropolis Adjusted Langevin Algorithm (MALA), simplified Manifold MALA, and Hamiltonian Monte Carlo: motivation, explanation and application - Chris Sherlock.
February 14th, 12.00pm (A54 Lecture Theatre)
Summary statistics for ABC model choice - Dennis Prangle.

2011

Decmber 13th, 3.00pm (A54 Lecture Theatre)
Constructing summary statistics for approximate Bayesian computation: semi-automatic ABC - Paul Fearnhead. [arXiv].
November 16th, 12.00pm (B35 John Nelder Room)
High-dimensional variable selection via tilting - Haeran Cho (London School of Economics).
June 17th, 12.00pm (Lab1 PSC)
Online inference and model selection using sequential Monte Carlo - Gareth Ridall.
May 24th, 12.00pm (A54 Lecture Theatre)
Simulation of mixed speed biochemical reactions using the linear noise approximation - Chris Sherlock
March 15th, 12.00pm (Lab1 PSC)
Reading group on “An explicit link between Gaussian fields and Gaussian Markov random fields: The SPDE approach” - led by Paul Fearnhead. Preprint.
February 15th, 12.15pm (Lab1 PSC)
Quantum Monte Carlo - Neil Drummond (Dept of Physics, Lancaster University)
January 18th, 1.00pm (A54 Lecture Theatre)
Optimal detection of changepoints with a linear computational cost - Rebecca Killick

2010

Decmember 7th, 12.00pm (A54 Lecture Theatre)
Using ABC for sequential Bayesian analysis - Dennis Prangle.
November 10th, 12.00pm (A54 Lecture Theatre)
Exact Inference for a Markov switching diffusion model with discretely observed data - Krzysztof Latuszynski (University of Warwick).
November 3rd, 12.00pm (A54 Lecture Theatre)
Bayesian variable selection using Lasso - Anastasia Lykou.
October 10th, 12.00pm (A54 Lecture Theatre)
Reading group on “Riemann manifold Langevin and Hamiltonian Monte Carlo methods” - led by Paul Fearnhead. [arXiv].
September 3rd, 1pm
Particle Filters for models with fixed parameters - Paul Fearnhead
February 16th
Reading group on Particle MCMC and the pseudo marginal algorithm - led by Gareth Ridall. Giorgos’ guide to the pseudo marginal algorithm;

2009

December 1st (lab1 PSC) - The random walk Metropolis: general criteria for the 0.234 acceptance rate rule - Chris Sherlock
November 3rd - Likelihood based inference for discretely observed diffusions - Giorgos Sermaidis
October 20th - Sequential Importance Sampling for General Diffusion Models - Paul Fearnhead
April 28th - Reading group on The Integrated Nested Laplace Approximation of Rue et al. (2009) - led by Chris Sherlock. Link to Rue Martino and Chopin (2009); suggested initial reading: Rue and Martino (2007).

2008

December 2nd - change point models and fault detection - Paul Fearnhead
October 28th - Optimal scaling of the random walk Metropolis - Part 1 - Chris Sherlock
June 2nd - Adaptive Sequential Monte Carlo Methods For Static Inference in Bayesian Mixture Analysis - Ben Taylor
May 13rd - The linear least squares prediction view of conjugate gradients - Joe Whittaker
March 18th - Perfect sampling for Random Trees - Hongsheng Dai
March 4th - An MCMC method for Approximate Bayesian Computation - Dennis Prangle
February 5th - Bayesian Analysis of ARMA and Transfer Function Time Series Models - Paul Smith

2007

November 28th - Power sums of lognormals - Chris Sherlock
November 21st - Asymptotic simultaneous bootstrap confidence bounds for simple linear regression lines - Thomas Jaki
October 31st - Using particle filters within MCMC - Paul Fearnhead

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