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.
Interim Programme - Subjected to Changes
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.
Associate Prof. François-Xavier Briol, University College London
Dr. Jack Jewson, Monash University
Dr Jeremias Knoblauch, University College London
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.
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
18 Jun 2025, 5.30pm - 7.30pm |
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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 |
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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 |
Categories | Regular registration (30 March - 15 June) |
|
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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) |
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.
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.
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
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.
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