Talks

2024

• June 27th: STOR-i boardroom
         Chris Sherlock
         Tuning pseudo-marginal Metropolis-Hastings: a vase or two faces? [work in progress]

• June 20th: STOR-i boardroom
         Claire Gormley
         Bayesian nonparametric modelling of network data [Bayesian Analysis]

• June 13th: STOR-i boardroom
         Saifuddin Syed
         Scaling inference of MCMC algorithms with parallel computing [JRSSB]

• June 6th: STOR-i boardroom
         Rui Zhang
         Unadjusted Barker as an SDE Numerical Scheme [arxiv]

• May 16th: STOR-i boardroom
         Wentao Li
         Optimal combination of composite likelihoods using approximate Bayesian computation with application to state-space models [arxiv]

• May 9th: STOR-i boardroom
         Gabriel Wallin
         Rotation to Sparse Loadings using Lp Losses and Related Inference Problems [arxiv]

• 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