Bayesian Inference

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20 papers in the last four weeks, up 300% on the four weeks before. 0.2% of all new papers.

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Latest papers 219

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Bayesian Inference of Nonlinear Malaria Dynamics in Ghana via an Ensemble Markov Chain Monte Carlo Sampler
May 30, 2026T. Ansah-Narh, Y. Asare Afrane, J. Bremang TandohEpidemic Forecasting Bayesian Inference
Ghana Space Science and Technology Institute, Ghana Atomic Energy Commission, P. O. Box LG 80, Legon, Accra, Ghana · Department of Medical Microbiology, University of Ghana Medical School, University of Ghana, Accra, Ghana
Annotation-Informed Block-Sparse Bayesian Modeling for cis-Expression Prediction
May 30, 2026Lei Huang, Hui Shen, Kuan-Jui Su +9Bayesian Inference Genomics
School of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS, USA · Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, USA · Texas Tech University Health Sciences Center, School of Medicine, Texas Tech University
Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo
May 29, 2026Xiaoyu Wang, Jonathan H. HugginsMarkov Chain Monte Carlo Stochastic Gradient Langevin Dynamics
Department of Mathematics & Statistics, Boston University · Faculty of Computing & Data Sciences, Boston University
Accurate Large-sample Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlo
May 29, 2026Yu Wang, Jie Ding, Jonathan H. HugginsMarkov Chain Monte Carlo Stochastic Gradient Langevin Dynamics
Department of Mathematics & Statistics, Boston University · Questrom School of Business, Boston University · Faculty of Computing & Data Sciences, Boston University
Bayesian Inference with Shaped Deep Non-linear MLPs
May 29, 2026Boris Hanin, Tianze JiangBayesian Neural Networks Multilayer Perceptrons
Princeton University
Is the Last Layer Sufficient for Uncertainty Quantification?
May 29, 2026Joseph Wilson, Chris van der Heide, Liam Hodgkinson +1Uncertainty Quantification Bayesian Inference
School of Mathematics and Physics, University of Queensland, Australia · ARC Training Centre for Information Resilience (CIRES), Brisbane, Australia · Department of Electrical and Electronic Engineering, University of Melbourne, Australia +1
Wasserstein Contraction of Coordinate Ascent Variational Inference
May 28, 2026Rocco Caprio, Adrien Corenflos, Sam PowerBayesian Inference Variational Inference
University of Warwick and Bocconi University. · University of Warwick. · University of Bristol.
Joint Model and Data Sparsification via the Marginal Likelihood
May 28, 2026Alexander Timans, Thomas Möllenhoff, Christian A. Naesseth +2Dataset Pruning Robust Regression
1UvA-Bosch Delta Lab, University of Amsterdam · 2RIKEN Center for AI Project, Tokyo, Japan · Department of Computer Science, Johns Hopkins University.
On the Construction and Implications of Low-Loss Valleys in LoRA-based Bayesian Inference
May 28, 2026Daniel Dold, Emanuel Sommer, Julius Kobialka +2Mode Connectivity Bayesian Inference
HTWG Konstanz · LMU Munich · Munich Center for Machine Learning (MCML)
Attention as In-Context Empirical Bayes: A Two-Stage View via Particle Dynamics
May 28, 2026Matthew Smart, Soumya Ganguly, Nilava Metya +2Self-Attention Empirical Bayes
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA · Department of Mathematics, Rutgers University, Piscataway, NJ, USA · Department of Physics and Astronomy, Rutgers University, Piscataway, NJ, USA +1
Meta-Attention: Bayesian Per-Token Routing for Efficient Transformer Inference
May 27, 2026Alan FerrariEfficient Transformer Inference Self-Attention
Knowledge Lab AG Zürich, Switzerland
Identifiable Bayesian Deep Generative Copulas with Unknown Layer Widths for Data with Arbitrary Marginal Distributions
May 26, 2026Joseph Feldman, Yuqi GuCopula Models Latent Variable Models
Department of Statistics and Data Science Washington University in St. Louis · Department of Statistics Columbia University
Gaussian Process-based learning with new MCMC-based implementation of Wishart prior on correlation matrix
May 26, 2026Kane Warrior, Dalia ChakrabartyBayesian Inference Covariance Estimation
Department of Mathematics University of York
Transformers Can Learn Posterior Predictive Distributions In-Context
May 26, 2026Gyeonghun Kang, Changwoo J. Lee, Xiang ChengBayesian Inference Transformer Attention
Department of Statistical Science, Duke University, Durham, NC, USA · Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
Amortized Factor Inference Networks for Posterior Inference
May 26, 2026Joohwan Ko, Justin DomkeAmortized Inference Bayesian Inference
Manning College of Information and Computer Sciences University of Massachusetts Amherst
Generalized Evidential Deep Learning: From a Bayesian Perspective
May 25, 2026Yuanye Liu, Yibo Gao, Yuanyang Chen +1Evidential Deep Learning Uncertainty Quantification
School of Data Science, Fudan University, Shanghai, China.
Hypothesis Generation and Inductive Inference in Children and Language Models
May 23, 2026Jeffrey Qin, Wasu Top Piriyakulkij, Zhuangfei Gao +4Bayesian Inference Scientific Hypothesis Generation
Computer Science University of Waterloo · Department of Computer Science Cornell University · Department of Computer Science Dalhousie University +1
Empirical Bayes Conformal Prediction for Vision and Language Models
May 22, 2026Jiapeng Zeng, Yogesh Prabhu, Zhanpeng Zeng +2VLM Evaluation Empirical Bayes
University of Wisconsin–Madison · University of California San Diego · Xiamen University
Uncertainty-aware classification and triage of structural heart disease using electrocardiography and echocardiography metrics
May 21, 2026Mitchel J. ColebankBayesian Neural Networks Uncertainty Quantification
Department of Mathematics, University of South Carolina,, Columbia, 29208, South Carolina, USA
An Evidence Hierarchy for Bayesian Object Classification via OSINT-Aided Heterogeneous Sensor Fusion
May 21, 2026Jan Nausner, Michael HubnerBayesian Inference Multimodal Sensor Fusion
Center for Digital Safety & Security, Austrian Institute of Technology GmbH (AIT), Giefinggasse 4, 1210 Vienna, Austria
PhylaFlow: Hybrid Flow Matching in Billera-Holmes-Vogtmann Tree Space for Phylogenetic Inference
May 21, 2026Yasha Ektefaie, Leo Cui, Shrey Jain +2Flow Matching Tree Search
Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard · Department of Biomedical Informatics, Harvard Medical School · Centennial High School +1
Truncated Neural Likelihood Estimation for Simulation-Based Inference in State-Space Models
May 20, 2026Kostas Tsampourakis, Víctor ElviraSimulation-Based Inference Parameter Estimation
School of Mathematics, University of Edinburgh,, EH9 3FD, U.K.
Position: The Time for Sampling Is Now! Charting a New Course for Bayesian Deep Learning
May 20, 2026Emanuel Sommer, David RügamerBayesian Neural Networks Efficient Neural Network Inference
Department of Statistics, LMU Munich, Munich, Germany · Munich Center for Machine Learning, Munich, Germany.
Corrected Integrated Laplace Approximation for Bayesian Inference in Latent Gaussian Models
May 19, 2026Jinlin Lai, Charles C. Margossian, Daniel R. SheldonLatent Variable Models Importance Sampling
Manning College of Information and Computer Sciences University of Massachusetts Amherst · Department of Statistics University of British Columbia
Posterior Contraction of Lévy Adaptive B-spline Regression in Besov Spaces
May 19, 2026Jeunghun Oh, Sewon Park, Jaeyong LeeBayesian Inference Nonparametric Regression
Department of Statistics, Seoul National University. · Department of Statistics, Sookmyung Women’s University.
AI4BayesCode: From Natural Language Descriptions to Validated Modular Stateful Bayesian Samplers
May 18, 2026Jungang Zou, Alex Ziyu Jiang, Qixuan ChenMarkov Chain Monte Carlo Bayesian Inference
Department of Biostatistics, Columbia University
Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors
May 18, 2026Aldric LabartheLatent Variable Models Bayesian Inference
CB, UNIGE · Centre Borelli, Université Paris-Saclay Gif-Sur-Yvette, FR · Department of Computer Science, University of Geneva Carouge, CH-1227
Agentic Chunking and Bayesian De-chunking of AI Generated Fuzzy Cognitive Maps: A Model of the Thucydides Trap
May 18, 2026Akash Kumar Panda, Olaoluwa Adigun, Bart KoskoComputational Cognitive Modeling Bayesian Inference
University of Southern California, Los Angeles, CA 90007, USA · Florida International University, Miami, FL 33199, USA
SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference
May 15, 2026Shi-ang Qi, Vahid Balazadeh, Michael Cooper +2Amortized Inference Survival Analysis
Vector Institute · University of Toronto · University of Alberta +1
Don't Stop Me Yet: Sampling Loss Minima via Dissipative Riemannian Mechanics
May 14, 2026Albert Kjøller Jacobsen, Leo Uhre Jakobsen, Johanna Marie Gegenfurtner +1Bayesian Neural Networks Dynamical Systems
Section for Cognitive Systems, Technical University of Denmark · Center for Quantum Information Physics, New York University

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Bayesian Inference of Nonlinear Malaria Dynamics in Ghana via an Ensemble Markov Chain Monte Carlo Sampler

T. Ansah-Narh, Y. Asare Afrane, J. Bremang Tandoh

Ghana Space Science and Technology Institute, Ghana Atomic Energy Commission, P. O. Box LG 80, Legon, Accra, Ghana · Department of Medical Microbiology, University of Ghana Medical School, University of Ghana, Accra, Ghana

Epidemic Forecasting Bayesian Inference Time Series Forecasting Probabilistic Forecasting

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Reliable quantification of malaria dynamics in sub-Saharan Africa is hindered by short, noisy, and spatially heterogeneous surveillance records. In Ghana, health-facility data from 2014 to 2023 reveal non-linear and age-specific fluctuations in hospital admissions, yet existing approaches struggle to capture stochastic variability or provide credible uncertainty bounds. This study develops a Bayesian nonlinear inference framework that integrates a cubic baseline with a damped oscillatory kernel, estimated via an affine-invariant ensemble Markov Chain Monte Carlo sampler. The framework accommodates limited data, models parameter uncertainty, and generates probabilistic forecasts for children under five years and individuals aged five years or more. Results show strong empirical adequacy (

R^2 = 0.9958

for

<5

years;

R^2 = 0.9956

for

\geq 5

years) with residual errors below

2\%

and well-mixed posteriors confirming convergence. District-level analysis reveals pronounced spatial heterogeneity, with coefficients of variation ranging from

<0.07

in urban centres such as Kumasi to

>3.3

in peripheral districts such as Mpohor and Bia East. Forecasts for 2024-2026 indicate a gradual resurgence: from 137,000 to 149,000 cases among children under five years and from 348,000 to 375,000 cases among older individuals, with uncertainty widening over time. By producing probabilistic forecasts, this Bayesian framework provides a principled tool for anticipating malaria fluctuations and strengthening data-driven decision-making in Ghana's national malaria control strategy.

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Latest papers 219