• Bayes Comp 2025 Singapore, 16-18 July

National University of Singapore


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About Bayes Comp

The biennial Bayes Comp meetings are organised by the Bayesian Computation Section of the International Society for Bayesian Analysis. Bayes Comp 2025 is the fourth conference in the series and is hosted by the Department of Statistics and Data Science at the National University of Singapore.


The Bayesian approach to learning from data has a very long history, but it has only flourished in modern applications with the use of modern computational tools. Bayes Comp 2025 gives a snapshot of the current state of the diverse and exciting field of Bayesian computation.

Contributed Talk/ Poster Submission has been closed on 1 March.

Key Dates


Call for Contributed Talk and Poster Session Abstracts

Early Bird Registration and NUS Hostels Booking
Online Registration (Regular Rates)

Now Open

NUS Hostel Booking Closes

14 May 2025

Online Registration Closes

15 June 2025

Satellite Workshop

16 June 2025

Main Conference

18 June 2025

Keynote Speakers

Sylvia Frühwirth-Schnatter

Pierre E. Jacob

Pierre E. Jacob

Emtiyaz Khan

Emtiyaz Khan

Invited Sessions

(Almost) Gradient-based Markov chain Monte Carlo Algorithms


Speakers
  • Dr. Francesca Romana Crucinio, King’s College London
  • Dr. Zhihao Wang, University of Copenhagen
  • Dr. Siddharth Vishwanath, University of California, San Diego

Chair/Organiser
  • Dr. Dootika Vats, Indian Institute of Technology Kanpur

Advanced Langevin Methods for Bayesian Sampling


Speakers
  • Dr. Sam Power, University of Bristol
  • Dr. Andi Wang, University of Warwick
  • Dr. Peter Whalley, ETH Zurich

Chair/Organiser
  • Dr. Neil K. Chada, City University of Hong Kong

Advances in efficient Bayesian inference for complex multivariate models


Speakers
  • Prof. Nadja Klein, Karlsruhe Institute of Technology, Germany
  • Dr. Luca Maestrini, Australian National University, Australia
  • Associate Prof. Hien Nguyen, La Trobe University, Australia

Chair/Organiser
  • Dr. Linda Tan, National University of Singapore

Advances in Heavy-Tailed Sampling: Bridging Theory and Practice


Speakers
  • Dr. Federica Milinanni KTH Royal Institute of Technology
  • Dr. Ye He, Georgia Institute of Technology
  • Dr. Chang-Han Rhee, Northwestern University

Chair/Organiser
  • Dr. Alex Shestopaloff, Queen Mary University of London
  • Dr. Jun Yang, University of Copenhagen

Advances in MCMC methods


Speakers
  • Prof. Alexandros Beskos, University College London
  • Prof. Antonietta Mira, Università della Svizzera italiana, Lugano and Insubria University, Como
  • Dr. Pariya Behrouzi, Wageningen University and Research, The Netherlands

Chair/Organiser
  • Prof. Maria De Iorio, National University of Singapore

Advances in Methodology for Sequential Monte Carlo in High Dimensions


Speakers
  • Prof. Sumeetpal Singh, University of Wollongong
  • Dr. Axel Finke, Loughborough University
  • Prof. Victor Elvira, University of Edinburgh

Chair/Organiser
  • Prof. Alexandros Beskos, University College London

Advances in Variational Inference


Speakers
  • Dr. Jeremias Knoblauch, University College London
  • Dr. Charles Margossian, Flatiron Institute
  • Dr. Kamélia Daudel, ESSEC Business School

Chair/Organiser
  • Prof. Randal Douc, Télécom Sud Paris

Approximate Methods for Accelerated Sampling


Speakers
  • Prof. Debdeep Pati, University of Wisconsin-Madison
  • Associate Prof. Yun Yang, University of Maryland, College Park
  • Dr. Leo Duan, University of Florida

Chair/Organiser
  • Asst. Prof. Yuexi Wang, University of Illinois Urbana-Champaign

Bayesian computation in astrophysics


Speakers
  • Dr. Avi Vajpeyi , University of Auckland
  • Dr. Alvin Chua, National University of Singapore
  • Dr. Joshua S. Speagle, University of Toronto

Chair/Organiser
  • Dr. Kate Lee, University of Auckland

Bayesian Federated Learning


Speakers
  • Dr. Louis Aslett, University of Durham, UK
  • Dr. Conor Hassan, Aalto University
  • Prof Jean-Michel Marin, Universite de Montpellier

Chair/Organiser
  • Prof. Christian Robert

Comparison theory for modern MCMC methods


Speakers
  • Rocco Caprio, University of Warwick
  • Dr. Qian Qin, University of Minnesota
  • Associate Prof. Giacomo Zanella, Bocconi University

Chair/Organiser
  • Dr. Andi Q. Wang, University of Warwick

Computation and model criticism for highly parametrized Bayesian models


Speakers
  • Prof. Maria De Iorio, National University of Singapore
  • Dr. Lucas Kock, National University of Singapore
  • Prof. Michael Smith, University of Melbourne

Chair/Organiser
  • Associate Prof. David Nott, National University of Singapore

Computation-enabled Bayesian inference and prediction


Speakers
  • Asst. Prof. Edwin Fong, Department of Statistics, University of Hong Kong
  • Asst. Prof. Yan Shuo Tan, Department of Statistics, National University of Singapore
  • Asst. Prof. Naoki Awaya, Faculty of Political Science and Economics, Waseda University, Japan

Chair/Organiser
  • Prof. Li Ma, Duke University, USA

Cutting Feedback: Methods and Applications in Bayesian Settings


Speakers
  • Dr. Mikolaj Kasprzak, ESSEC Business School, Paris
  • Associate Prof. David Nott, National University of Singapore
  • Dr. Pantelis Samartsidis, MRC Biostatistics Unit, University of Cambridge

Discussant
  • Prof. Daniela De Angelis, MRC Biostatistics Unit, University of Cambridge

Chair
  • Prof. Daniela De Angelis, MRC Biostatistics Unit, University of Cambridge

Organisers
  • Dr. Anne Presanis

Flexible approximate inference methods


Speakers
  • Prof. Robert Kohn, University of New South Wales
  • Dr. Linda Tan, National University of Singapore
  • Dr. David Chen, National University of Singapore

Chair/Organiser
  • Associate Prof. David Nott, National University of Singapore

High-dimensional discrete model search


Speakers
  • Prof. Yves Atchadé, Boston University
  • Dr. Hyunwoong Chang, University of Texas at Dallas
  • Dr. Déborah Sulem, Universitá de la Svizzera Italiana

Chair/Organiser
  • Prof. David Rossell, Pompeu Fabra University

Manifold Markov chain Monte Carlo and beyond


Speakers
  • Dr. Mareike Hasenpflug, University of Passau
  • Dr. Cameron Bell, University of Warwick
  • Dr. Björn Sprungk, University of Freiberg

Chair/Organiser
  • Prof. Daniel Rudolf, University of Passau

Model Misspecification in Simulation-based Inference


Speakers
  • Associate Prof. François-Xavier Briol, University College London, UK
  • Prof. Paul Bürkner, TU Dortmund University, Germany
  • Dr. Antoine Wehenkel, Apple

Chair/Organiser
  • Dr. Ayush Bharti, Aalto University, Finland

Optimization and Control for Sampling


Speakers
  • Dr. Lorenz Richter, Zuse Institute Berlin
  • Dr. Nikolas Nusken, King's College London
  • Dr. Nikolay Malkin, University of Edinburgh

Chair/Organiser
  • Associate Prof. Alexandre Thiery, National University of Singapore

Parallel Computations for Markov chain Monte Carlo


Speakers
  • Prof. Dootika Vats, Department of Mathematics and Statistics, Indian Institute of Technology
  • Asst. Prof. Lu Yu, Department of Data Science, City University of Hong Kong
  • Dr. Sebastiano Grazzi, Department of Decision Sciences, Bocconi University

Chair
  • Dr. Charles Margossian, Flatiron Institute, Simons Foundation

Organisers
  • Dr. Sebastiano Grazzi

PDMPs in Practice


Speakers
  • Prof. Kengo Kamatani, The Institute of Statistical Mathematics
  • Mr. Luke Hardcastle, University College London
  • Dr. Jere Koskela, University of Newcastle

Discussant
  • Dr. Samuel Livingstone, University College London

Chair
  • Dr. Samuel Livingstone, University College London

Organisers
  • Mr. Luke Hardcastle

Recalibration methods for improved Bayesian inference from approximate models


Speakers
  • Dr. Jeong E (Kate) Lee, University of Auckland
  • Dr. Guilherme Souza Rodrigues, University of Brasilia
  • Adam Bretherton, Queensland University of Technology

Discussant
  • Prof. Scott Sisson, University of New South Wales

Chair/Organiser
  • Dr. Joshua Bon, Université Paris Dauphine

Recent advances in Sequential Monte Carlo methods


Speakers
  • Prof. Pierre Del Moral, INRIA Bordeaux
  • Dr. Adrien Corenflos, University of Warwick
  • Asst. Prof. Ning Ning, Texas A&M University

Chair/Organiser
  • Dr. Hai-Dang Dau, National University of Singapore

Scalable Causal Inference


Speakers
  • Prof. Michael J. Daniels, University of Florida
  • Prof. Hui Guo, University of Manchester
  • Dr. Jack Kuipers, ETH Zürich

Chair/Organiser
  • Prof. Maria De Iorio, National University of Singapore
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Simulation-based Bayesian inference: efficiency, robustness, and theoretical results


Speakers
  • Prof. David Frazier, Monash University
  • Asst. Prof. Yuexi Wang, University of Illinois Urbana-Champaign
  • Ryan Kelly, PhD student, Queensland University of Technology

Discussant
  • Dr. Clara Grazian, University of Sydney

Chair/Organiser
  • Prof. Chris Drovandi, Queensland University of Technology

Surrogate Models and Kernel Methods


Speakers
  • Prof. Dino Sejdinovic, University of Adelaide
  • Dr. Karina Koval, Heidelberg University
  • Dr. Aretha Teckentrup, University of Edinburgh

Chair/Organiser
  • Prof. Chris. J. Oates, Newcastle University

Variational Bayes for Uncertainty Quantification


Speakers
  • Associate Prof. Trevor Campbell, University of British Columbia
  • Associate Prof. Tamara Broderick, Massachusetts Institute of Technology
  • Dr. Yuling Yao, University of Texas, Austin

Chair/Organiser
  • Dr. Ryan Giordano and Dr. Alex Strang, University of California, Berkeley

Organising Committee

Local organising committee

  • David Nott (Chair) (NUS)
  • Li Cheng (NUS)
  • Michael Choi (NUS)
  • Vik Gopal (NUS)
  • Jeremie Houssineau (NTU)
  • Maria De Iorio (NUS)
  • Adrian Roellin (NUS)
  • Linda Tan (NUS)
  • Yan Shuo Tan (NUS)
  • Alex Thiery (NUS)
  • Xin Tong (NUS)
  • Wu Zhengxiao (SMU)

Scientific program committee

  • David Frazier and Leah South (Co-chairs)
  • Pierre Alquier (ESSEC Business School)
  • Taeryon Choi (Korea U)
  • Francesca Crucinio (University of Torino and Collegio
    Carlo Alberto)
  • Cathy Chen (Feng Chia U)
  • David Gunawan (U of Wollongong)
  • Kengo Kamatani (ISM)
  • Kate Lee (Auckland)
  • Kerrie Mengersen (QUT)
  • Christian P Robert (Paris Dauphine/Warwick)
  • Veronika Rockova (Chicago Booth)
  • Judith Rousseau (Paris Dauphine/Oxford)
  • Sumeetpal Singh (U of Wollongong)
  • Mike So (HKUST)
  • Dootika Vats (IIT Kanpur)

Programme

Conference Days

  • Day 1 - 16 June 2025
  • Day 2 - 17 June 2025
  • Day 3 - 18 June 2025
  • Day 4 - 19 June 2025
  • Day 5 - 20 June 2025

Interim Programme - Subjected to Changes

  • Day 1 - 16 June 2025
    08:00am - 09:00am Registration
    09:00am – 10:30am Satellite workshops
    10:30am – 11:00am Morning Break
    11:00am – 12:30pm Satellite workshops
    12:30pm – 02:00pm Lunch Break
    02:00pm – 03:30pm Satellite workshops
    03:30pm – 04:00pm Afternoon Break
    04:00pm – 06:00pm Satellite workshops
  • Day 2 - 17 June 2025
    08:00am – 09:00am Registration
    09:00am – 10:30am Satellite workshops
    10:30am – 11:00am Morning Break
    11:00am – 12:30pm Satellite workshops
    12:30pm – 02:00pm Lunch Break
    02:00pm – 03:30pm Satellite workshops
    03:30pm – 04:00pm Afternoon Break
    04:00pm – 06:00pm Satellite workshops
  • Day 3 - 18 June 2025
    08:00am – 09:00am Registration
    09:00am – 09:05am Welcome Address
    09:05am – 09:10am Opening Address
    09:10am – 10:10am Keynote Lecture I
    10:10am – 10:30am Morning Break
    10:30am – 12:00pm Parallel Sessions
    12:00pm – 01:40pm Lunch Break
    01:40pm – 03:10pm Parallel Sessions
    03:10pm – 03:30pm Afternoon Break
    03:30pm – 05:30pm Parallel Sessions
    05:30pm – 07:30pm Ice breaker and poster session
  • Day 4 - 19 June 2025
    08:00am – 09:00am Registration
    09:00am – 10:00am Keynote Lecture II
    10:00am – 10:20am Morning Break
    10:20am – 11:50am Parallel Sessions
    11:50am – 01:40pm Lunch Break
    01:40pm – 03:10pm Panel Discussion
    03:10pm – 03:30pm Afternoon Break
    03:30pm – 05:30pm Parallel Sessions
    05:30pm – 07:30pm Light dinner and poster session
  • Day 4 - 19 June 2025
    08:00am – 09:00am Registration
    09:00am – 10:00am Keynote Lecture III
    10:00am – 10:20am Morning Break
    10:20am – 11:50am Parallel Sessions
    11:50am – 01:10pm Lunch Break
    01:10pm – 02:40pm Parallel Sessions
    02:40pm – 3:00pm Afternoon Break
    03:00pm – 04:30pm Parallel Sessions
    04:30pm – 04:50pm Awards and closing remarks

Satellite Workshops

Satellite Workshop 1

Bayesian Computation and Inference with Misspecified models

https://postbayes.github.io/BayesMisspecificationSatellite/

A common justification for the use of Bayesian inference is that Bayes’ theorem is the optimal way to update beliefs based on new observations, and that representing beliefs through a posterior distribution is desirable for uncertainty quantification. However, standard posterior distributions are only meaningful when the model or likelihood is well-specified, which is not the case in the presence of outliers, adversarial contaminations, or faulty measurement instruments. This realisation has led to an increased focus on generalisations of Bayesian inference which aim at obtaining ‘generalised posterior distributions’ providing some representation of uncertainty but also overcoming some the lack of robustness of standard posteriors. The aim of this workshop will be to give a broad overview of this topic, touching on both foundational questions and algorithmic advances, and inviting the Bayesian Computation community to take a more active role in solving some of the remaining open challenges in this area.



Organisers

Associate Prof. François-Xavier Briol, University College London
Dr. Jack Jewson, Monash University
Dr Jeremias Knoblauch, University College London

Satellite Workshop 2

Bayesian Methods for Distributional and Semiparametric Regression

https://kleinlab-statml.github.io/subpages_research/events/BayesComp2025.html

This satellite workshop aims to bridge the gap between computational and theoretical advancements and modern applications in Bayesian methods for distributional and semiparametric regression by bringing together leading experts in the field. Participants will benefit from talks that cover key tasks, such as model formulation, variable selection, inference techniques and associated computational challenges and practical implications. By highlighting the latest developments, this workshop will provide an overview of current research advancements, fostering discussions that inspire collaboration and innovation in advanced Bayesian regression.



Organisers

Prof. Nadja Klein, Scientific Computing Center, Karlsruhe Institute of Technology, Germany
Dr. Lucas Kock, Department of Statistics and Data Science, National University of Singapore, Singapore

Poster Sessions

Poster Session Venue

18 Jun 2025, 5.30pm - 7.30pm

Poster

Title

Presenter

A01

On A Modified Adaptive Progressive Censoring Scheme and Related Inferences

Abhimanyu Singh Yadav

Banaras Hindu University

A02

Computationally Efficient Multi-Level Gaussian Process Regression for Functional Data Observed Under Completely Or Partially Regular Sampling Designs

Adam Gorm Hoffmann

University of Copenhagen

A03

Advancing Estimation of Average Relative Humidity in The Usa Using Neutrosophic Stratified Ranked Set Sampling

Anamika Kumari

Manipal Academy of Higher Education

A04

MCMC Importance Sampling via Moreau-Yosida Envelopes

Apratim Shukla

IIT Kanpur

A05

Lower Bounds of Total Variation Distances for Multivariate Conditional Metropolis-Hastings Samplers

Arka Banerjee

IIT Kanpur

A06

Generalized Exponential Proportional Hazard Model for Joint Modelling of Longitudinal and Survival Data

Avinash Kumar

Banaras Hindu University

A07

Integrating Normative and Survival Modeling in MS via Bayesian Modularized Inference

Bernd Taschler

University of Oxford

A08

Computational and Statistical Guarantees for Star-Structured Variational Inference

Bohan Wu

Columbia University

A09

Particle-Based Inference for Continuous-Discrete State Space Models

Christopher Stanton

University College London

A10

Look Ma, No Sampling!

Colin Fox

University of Otago

A11

The ARR2 Prior: Flexible Predictive Prior Definition for Bayesian Auto-Regressions

David Kohns

Aalto University

A12

Exact Sampling of Spanning Trees Via Fast-forwarded Random Walks

Edric Tam

Stanford University

A13

Convergence of Statistical Estimators via Mutual information Bounds

EL Mahdi Khribch

Essec Business School

A14

Proper Random Walks An Enhanced Approach To Robust Spline Smoothing

Eman Kabbas

King Abdullah University of Science and Technology

A15

Calibration of Dose-Agnostic Priors for Bayesian Dose-Finding Trial Designs with Joint Outcomes

Emily Alger

Institute of Cancer Research

A16

Extending Bayesian Causal forests for Longitudinal Data Analysis: A Case Study in Multiple Sclerosis

Emma Prevot

University of Oxford

A17

Control Variate-Based Stochastic Sampling From The Probability Simplex

Francesco Barile

University of Milano-Bicocca

A18

Zero-Order Parallel Sampling

Francesco Pozza

Bocconi University

A19

Scalable MCMC Methods for Bayesian Blind Deconvolution

Guillermina Senn

Norges Teknisk-Naturvitenskapelige Universitet

A20

Online Filtering for Discretely-Observed Diffusions with Blocked Particle Filters

Hai-Dang Dau

National University of Singapore

A21

Orthogonal Polynomials Are All You Need: Skewed Posterior Approximations with Variational Bayes

Hans Montcho

King Abdullah University of Science and Technology

A22

Causal Inference for Longitudinal Multilevel Data - A Bayesian Semiparametric G-Computation Approach

Huixia Savannah Wang

Umeå School of Business, Economics and Statistics

A23

Enhanced Gaussian Process Surrogates for Optimization and Sampling By Pure Exploration

Hwanwoo Kim

Duke University

A24

Mixtures of Directed Graphical Models for Discrete Spatial Random Fields

J. Brandon Carter

University of Texas At Austin

A25

Bayesian Analysis of Clustered Data Within a Semi-Competing Risks Framework

Jinheum Kim

University of Suwon

A26

Bayesian Robust Inference for Doubly-intractable Distributions via Score Matching

Jiongran Wang

Texas A&M University

A27

On The forgetting of Particle Filters

Joona Karjalainen

University of Jyväskylä

A28

Sampling from High-Dimensional, Multimodal Distributions Using Automatically Tuned, Tempered Hamiltonian Monte Carlo

Joonha Park

University of Kansas

A29

Learning Misspecified Ode Models from Heterogeneous Data with Biology-informed Gaussian Processes

Julien Martinelli

Université De Bordeaux

A30

Nonparametric Bayesian Additive Regression Trees for Prediction and Missing Data Imputation in Longitudinal Studies

Jungang Zou

Columbia University

B31

Bayesian Combined Statistical Decision Limits with Covariates

Lian Mae T. Tabien

University of The Philippines Diliman

B32

Robust and Conjugate Gaussian Process Regression

Matias Altamiran

University College London

B33

Real-Time forecasting Livestock Disease Outbreaks with Approximate Bayesian Computation

Meryl Theng

The University of Melbourne

B34

Bayesian Crossover Trial with Binary Data and Extension to Latin-Square Design

Mingan Yang

University of New Mexico,

B35

The Polynomial Stein Discrepancy for Assessing Moment Convergence

Narayan Srinivasan

Queensland University of Technology

B36

Improving Variable Selection Properties By Using External Data

Paul Rognon-Vael

Universitat Pompeu Fabra

B37

Parallel Affine Transformation Tuning: Drastically Improving The Effectiveness of Slice Sampling

Philip Schär

Friedrich Schiller University Jena

B38

A Simple Bayesian Solution to Reducing The Factor Zoo

Robert I. Webb

University of Virginia

B39

Adaptive Shrinkage With A Nonparametric Bayesian Lasso

Santiago Marin

The Australian National University

B40

Iterated forward Scheme to Construct Proposals for Sequential Monte Carlo Algorithms

Sylvain Procope-Mamert

Université Paris-Saclay

B41

Real-Time Estimation of Gas Emission Sources Using Particle Filters and Neural Networks

Thomas Newman

Lancaster University

B42

Bayesian Computation for Partially Observed SPDEs

Thorben Pieper-Sethmacher

Delft University of Technology

B43

A General Framework for Probabilistic Model Uncertainty

Vik Shirvaikar

University of Oxford

B44

Bayesian Semiparametric Likelihood-Based Regression Inference for Optimal Dynamic Treatment Regimes

Weichang Yu

The University of Melbourne

B45

Robust and Conjugate Spatio-Temporal Gaussian Processes

William Laplante

University College London

B46

Information-Theoretic Classification of The Cutoff Phenomenon in Markov Processes

Youjia Wang

National University of Singapore

B47

A Novel Approach for Forecasting Non-Stationary Time Series: Utilization of a Variational Autoencoder Reflecting Seasonal Patterns

Young Eun Jeon

andong National University

B48

Robust Bayesian Methods Using Amortized Simulation-Based Inference

Yuyan Wang

National University of Singapore

B49

A Framework for Measuring Dependence of Partitions On Covariates in Mixture Models

Zhaoxi Zhang

University of Edinburgh

B50

Ensemble Filtering in Nonlinear Dynamical Systems: A Diffusion-based Approach

Zhidi Lin

National University of Singapore

B51

Sample Continuation in Bayesian Hierarchical Model via Variational Inference

Zilai Si

Northwestern University

B52

Nested Kernel Quadrature

Zonghao Chen

University College London



19 Jun 2025, 5.30pm - 7.30pm

Poster

Title

Presenter

A01 Challenges and Insights from Non-Uniform Polytope Sampling A. Stratmann Forschungszentrum Jülich
A02 Bayesian Analysis of Historical Functional Linear Models A.E. Clark University of Cape Town
A03 Dual Multi-Outcome Transformation Causal Estimation Biomarkers Discovery Framework Using DNA Methylation Against RNA and Proteins Expression Ala’a El-Nabawy Northumbria University
A04 Mixing Time Bounds for The Gibbs Sampler Under Isoperimetry Alexander Goyal Imperial College London
A05 Variational Bayes Inference for Simultaneous Autoregressive Models with Missing Data Anjana Wijayawardhana University of Wollongong
A06 Approximating Bayesian Leave-One-Group-Out Cross-Validation Anna Elisabeth Riha Aalto University
A07 Computationally Efficient Bayesian Joint Modeling of Mixed-Type High-Dimensional Multivariate Spatial Data Arghya Mukherjee IIT Kanpur
A08 Decision Making Under Model Misspecification: DRO with Robust Bayesian Ambiguity Sets Charita Dellaporta University of Warwick and University College London
A09 Radial Neighbors for Provably Accurate Scalable Approximations of Gaussian Processes Cheng Li National University of Singapore
A10 Optimal Design of the Randomized Unbiased Monte Carlo Estimators Chihoon Lee Stevens Institute of Technology
A11 A More Consistent Approximate Bayesian Framework for Learning the Optimal Action-Value Function in MDPs Chon Wai Ho University of Cambridge
A12 Bayesian Survival Model Updating Using Power Prior: Application to Cancer Data Analysis Dahhay Lee Yonsei University
A13 Bayesian Inference of Time-Varying Reproduction Number From Epidemic and Phylogenetic Data Using Particle MCMC Dr Alicia Gill University of Oxford
A14 Approximate Bayesian Fusion Filippo Pagani University of Warwick
A15 The Spectrum of the Optimal Self-Regenerative and independent Metropolis Markov Chains with Applications to MCMC Florian Maire Université de Montréal
A16 Detecting Conflicts in Bayesian Hierarchical Models Using Score Discrepancies Fuming Yang University of Cambridge
A17 Decoding Socio-Economic inequalities in Uttar Pradesh: A Spatio-Temporal Study with Wroclaw Taxonomy and K-Means Clustering Techniques Gaurav Chandrashekhar Hajare Manipal Academy of Higher Education
A18 Scalable Bayesian Factor Models for Dimensionality Reduction in High-Dimensional Multimodal Data with Structured Missingness George Hutchings University of Oxford
A19 Simulation-Based Inference for Stochastic Nonlinear Mixed-Effects Models with Applications in Systems Biology Henrik Häggström Chalmers University of Technology and University of Gothenburg
A20 Bayesian Inference of a Nearest Neighbor Gaussian Process Model for Pooled Genetic Data Imke Botha University of Melbourne
A21 Branching Stein Variational Gradient Descent Isaías Bañales Kyoto University
A22 Novel Bayesian Algorithms for ARFIMA Long-Memory Processes: A Comparison Between MCMC and ABC Approaches James Gabor University of Sydney
A23 Scalable Bayesian Causal Inference for Uplift Modeling with Conformal Prediction Jeong in Lee Inha University
A24 Pareto Smoothed ABC-SMC Jia Le Tan University of Warwick
A25 Bayesian Neural Network Optimisation for Multi-Trait Parental Selection to Enhance Economic Gains in Animal and Plant Breeding Jia Liu Australian National University
A26 Accelerating Bayesian Inference for Sequential Data Batches in Epidemic Transmission Models Joel Kandiah University of Cambridge
A27 Reliable Chemical Toxicity Assessment Via Transformer Models and Conformal Prediction Methodology Junhee Kim Inha University
A28 Post-Bayesian Inference for Misspecified Cosmological Models Kai Lehman Ludwig Maximilian University of Munich
A29 A Data-Driven Approach To Bayesian Hierarchical Modelling and Bayesian Neural Networks for Critical Illness Risk Prediction Kaitlyn Louth University of Edinburgh and Heriot-Watt University,
A30 A Formal Method for Verifying Bayes Factor Computations Using Half-Order Moments Kensuke Okada The University of Tokyo
B31 Symmetrizing Variational Monte Carlo Solvers for The Many-Electron Schrödinger Equation Kevin Han Huang University College London
B32 Validation of Bayesian Population and Sub-Population Estimates Lauren Kennedy University of Adelaide
B33 Ensemble Control Variates Long M. Nguyen Queensland University of Technology
B34 Bayesian Perspectives on Data Augmentation for Deep Learning Madi Matymov King Abdullah University of Science and Technology
B35 Cohering Disaggregation and Uncertainty Quantification for Spatially Misaligned Data Man Ho Suen University of Edinburgh
B36 Scaling Laws for Uncertainty in Deep Learning Mattia Rosso King Abdullah University of Science and Technology
B37 GANs Secretly Perform Approximate Bayesian Model Selection Maurizio Filippone King Abdullah University of Science and Technology
B38 Projected and Updated L0 Criteria for Variable Selection in High-Dimensional and Large-Sample Regression Models Maxim Fedotov Universitat Pompeu Fabra
B39 Bayesian Ranking of Treatments for Static Evaluation and Adaptive intervention Miguel R. Pebes-Trujillo Nanyang Technological University
B40 Variable Selection and Estimation Using Nonlocal Prior Mixtures for Data with Widely Varying Effect Sizes Nilotpal Sanyal University of Texas at El Paso
B41 Adversarial Robustification of Bayesian Prediction Models Pablo García Arce Instituto de Ciencias Matemáticas
B42 Deterministic Posterior Approximations in Streaming Data Scenarios Patric Dolmeta Universita' di Torino
B43 Bayesian Analysis of Cumulative Damage Models with Continuous Damage Functions Rijji Sen University of Calcutta
B44 Creating Rejection-Free Samplers By Rebalancing Skew-Balanced Jump Processes Ruben Seyer Chalmers University of Technology and University of Gothenburg
B45 Exploring Bimodal Fertility Patterns: A Bayesian Mixture Density Approach Shambhavi Singh Banaras Hindu University
B46 Approximate Maximum Likelihood Estimation with Local Score Matching Sherman Khoo University of Bristol
B47 Digital Biomarker Construction Via Bayesian Motif-Based Clustering Method of Freeliving Physical Activity Data From Wearable Devices Sin-Yu Su National Taiwan University
B48 Learning The Learning Rate in Generalized Bayesian Inference Sitong Liu University of Oxford
B49 BMW: Inlier Prone Bayesian Models for Correlated Bivariate Data Sumangal Bhattacharya Indian Statistical Institute Delhi
B50 AI-Powered Bayesian Inference Veronika Rockova University of Chicago
B51 Bayesian Dynamic Generalized Additive Model for Mortality During COVID-19 Pandemic Wei Zhang Bocconi University
B52 Localized Transfer Learning in Non-Stationary Spatial Model with PM2.5 Data Wenlong Gong University of Houston System
B53 Bayesian Multivariate Spatial Lgcp Modeling with inla-Spde, with Application To Human Microbiome Imaging Data Yan Gong Harvard T.H. Chan School of Public Health
B54 Predictive Performance of Power Posteriors Yann McLatchie University College London

Registration Fees

Categories Regular registration
(30 March - 15 June)
Main Conference (16 - 20 June 2025) (Does not include access to Satellite Workshops)
Non-Students Member of ISBA S$710
Member of SBSS S$850
Non-member of ISBA/SBSS S$930
Students Member of ISBA S$460
Member of SBSS S$500
Non-member of ISBA/SBSS S$530
Satellite Workshops (16 - 20 June 2025) (Does not include access to Main Conference)
Non-Students Member of ISBA S$460
Member of SBSS S$520
Non-member of ISBA/SBSS S$600
Students Member of ISBA S$220
Member of SBSS S$260
Non-member of ISBA/SBSS S$300
Bayes Comp NUS Student Hostel Package
(15 June - 21 June)
(Limited rooms available)
S$260 for Student Hostel Package (5 days, 4 nights)
S$370 for Student Hostel Package (7 days, 6 nights)
NUS University Town (UTown), Kent Ridge Campus
Register by 14 April 2025
Hotels (within 10km of NUS)
Direct booking with the hotel. Refer to link.
(Breakfast to be purchased at the front desk)
S$145++ to S$195++ Per Night
Park Avenue Rochester
Citadines Science Park
(Key in "BAYESCOMP25" under Promotion field to enjoy conference rates)
Register Now

NUS UTown Student Hostels

Standard Rooms (Air-conditioned)

A single-person living space furnished with a single bed, a ceiling fan, a writing desk and chair, a bookshelf, a wardrobe and a mobile pedestal. Shower and toilet facilities as well as kitchenettes (at selected levels) are located along the common corridors or within the apartments. Kitchenettes are equipped with stoves and other kitchen appliances where residents can cook their own meals instead of eating out.

UTown Room
  1. Student Hostel Rooms at NUS UTown, Kent Ridge Campus:
    • As our hostels are gender specific, please indicate your birth gender during registration.
    • If you are planning to check-in later than 15 June, please indicate in remarks when selecting your housing option.
    • There will be no refund or discounted rate for late check-in or early check-out.
    • Registration will close on 14 April 2025.

    Our budget-friendly accommodation option offers single occupancy rooms with shared bathroom facilities and come with single bed, one pillow, one blanket, one towel, a set of basic toiletries, a refillable water bottle and free wi-fi.



Off-Campus Accommodation

Optional External Hotel for all other participants (to book directly with hotel)
  • Park Avenue Rochester
  • Citadines Science Park Singapore
Bayescomp Map

Park Avenue Rochester
(8 min to UTown)

$195++ per room
Superior King
$230++ per room
Superior Double
With breakfast
*To be purchased at the front desk
$185++ per room
Superior King
$190++ per room
Superior Double
No breakfast

Citadines Science Park
(9 min to UTown)

$170++ per room
Studio Twin
$190++ per room
Studio Executive - King Bed
$290++ per room
1 Bedroom Executive
w/ Fully equipped Kitchenette w/ Washer & Dryer

No breakfast
Wifi included
Book Citadines Science Park
Key in "BAYESCOMP25" under Promotion field to enjoy conference rates

Junior Travel Support

Bayes Comp 2025 will offer partial support for travel of selected PhD students and junior researchers who are presenting talks or posters to attend the meeting. This partial support is made possible by sponsorship from the International Society for Bayesian Analysis (ISBA) and the Bayesian Computation Section of ISBA

The amount of support will be up to USD250. Reimbursement will occur after the meeting, and receipts will be required. The instructions for how to obtain reimbursement will be shared with the successful applicants

Childcare Support

Bayes Comp 2025 will offer partial support for selected PhD students and junior researchers with young children (less than 13 years old) attending the meeting. This partial support is made possible by sponsorship from the International Society for Bayesian Analysis (ISBA) and the Bayesian Computation Section of ISBA

The amount of support will be up to USD250. Reimbursement will occur after the meeting, and receipts will be required to substantiate the amount spent on childcare. The instructions for how to obtain reimbursement will be shared with the successful applicants.

Contact Us

For general enquiries, please email to stabox20@nus.edu.sg

If you have questions regarding your registration and ticketing, please send an email to ceuevents@nus.edu.sg

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