Bayesian Statistics

Bayesian Nonparametric Mixture Models

Development of Dirichlet process and Pitman-Yor process mixtures for flexible clustering with unknown component numbers.

RESEARCH GAP FRONTIERS

Variational Inference Approximation Methods

Advanced techniques for mean-field and structured variational inference to approximate intractable posterior distributions.

RESEARCH GAP FRONTIERS

Hamiltonian Monte Carlo Sampling

Gradient-based MCMC methods using Hamiltonian dynamics for efficient exploration of high-dimensional parameter spaces.

RESEARCH GAP FRONTIERS

Gaussian Process Regression Inference

Bayesian approaches to Gaussian processes with automatic relevance determination and hyperparameter optimization.

RESEARCH GAP FRONTIERS

Bayesian Deep Neural Networks

Probabilistic frameworks for uncertainty quantification in neural networks through variational approximations.

RESEARCH GAP FRONTIERS

Empirical Bayes Hyperparameter Selection

Data-driven methods for estimating prior hyperparameters through marginal likelihood maximization.

RESEARCH GAP FRONTIERS

Hierarchical Bayesian Spatial Modeling

Multi-level Bayesian models incorporating spatial structure and random effects for geographic data analysis.

RESEARCH GAP FRONTIERS

Approximate Bayesian Computation Methods

Likelihood-free inference techniques for complex models where likelihood evaluation is computationally intractable.

RESEARCH GAP FRONTIERS

Bayesian Causal Inference Networks

Probabilistic graphical models for estimating causal effects with directed acyclic graph representations.

Reversible Jump Markov Chain Monte Carlo

Trans-dimensional MCMC methods for model comparison and selection across varying parameter space dimensions.

Bayesian Optimization for Experimental Design

Sequential design strategies using expected improvement and Gaussian process surrogates for expensive simulations.

Copula-based Bayesian Dependence Modeling

Flexible models for multivariate dependencies using Bayesian inference on copula structures.

Bayesian Nonparametric Time Series Analysis

Infinite-dimensional models for temporal data including infinite hidden Markov models and nonparametric autoregression.

Graphical Model Structure Learning

Bayesian methods for discovering conditional independence structures in high-dimensional graphical models.

Normalizing Flows for Density Estimation

Deep generative models using invertible transformations for flexible posterior approximation in Bayesian inference.

Bayesian Functional Data Analysis

Probabilistic models for infinite-dimensional functional data with basis expansion and smoothness priors.

Sequential Importance Sampling Filters

Particle filter methods for state-space models with adaptive resampling and proposal mechanisms.

Bayesian Survival Analysis Methods

Flexible models for censored outcomes incorporating nonproportional hazards and competing risks.

Variational Autoencoder Inference

Probabilistic deep learning models combining variational inference with neural network encoders and decoders.

Bayesian Model Averaging and Selection

Methods for uncertainty propagation across multiple models using Bayesian model probabilities.

Expectation Propagation Algorithms

Message-passing algorithms for approximate inference in probabilistic graphical models.

Bayesian Additive Regression Trees

Nonparametric ensemble methods combining tree-based models with Bayesian regularization priors.

Stick-Breaking Processes and Applications

Theoretical development and applications of normalized random measures in Bayesian nonparametrics.

Bayesian Inverse Problems Regularization

Principled approaches to ill-posed inverse problems using Bayesian inference with regularization priors.

Adaptive Markov Chain Monte Carlo Methods

Self-tuning MCMC algorithms that adapt proposal distributions and step sizes during simulation.

Bayesian Nonlinear Regression Modeling

Flexible models for complex nonlinear relationships using splines, kernels, and additive structures.

Latent Variable Factor Analysis Models

Bayesian dimensionality reduction through latent factor models with structured covariance assumptions.

Quantum Computing Bayesian Inference

Novel algorithms leveraging quantum circuits for accelerated Bayesian posterior computation and sampling.

Bayesian Recurrent Neural Networks

Uncertainty quantification in sequential models through Bayesian treatment of weights and activations.

Prior Elicitation from Expert Opinion

Methods for translating expert knowledge into meaningful prior distributions using structured protocols.

Bayesian Genomic Selection Methods

High-dimensional genetic prediction using Bayesian regression with shrinkage priors for genomic markers.

Gaussian Markov Random Field Models

Sparse precision matrix methods for efficient inference in spatial and spatio-temporal applications.

Bayesian Quantile Regression Methods

Probabilistic approaches to estimating conditional quantiles with asymmetric error distributions.

Coalescent Process Bayesian Phylogenetics

Inferring evolutionary trees and population parameters from DNA sequences using coalescent priors.

Bayesian Sparse Signal Detection

Methods for identifying sparse patterns in high-dimensional data using spike-and-slab priors.

Federated Bayesian Learning

Distributed inference methods for decentralized data across multiple agents with privacy constraints.

Bayesian Multivariate Volatility Modeling

Time-varying covariance models for financial returns using stochastic volatility and copula structures.

Neural Density Estimation Networks

Deep learning approaches for flexible density estimation in Bayesian generative models.

Bayesian Missing Data Imputation

Multiple imputation methods accounting for missing data mechanisms in likelihood inference.

Infinite Mixture of Experts Models

Nonparametric mixtures of local regression models with infinite-dimensional partitioning structures.

Posterior Contraction Rate Theory

Asymptotic theory characterizing convergence rates of Bayesian posteriors to true parameters.

Bayesian Spatial Point Process Models

Log Gaussian Cox processes and marked point patterns for analyzing clustered spatial events.

Sequential Monte Carlo Sampling Filters

Advanced particle filtering with reweighting schemes for non-linear and non-Gaussian state-space models.

Bayesian Sensitivity Analysis Methods

Robustness assessment of Bayesian inference to prior specification and model assumptions.

Cylindrical and Directional Bayesian Models

Specialized distributions and inference methods for angular and circular data on manifolds.

Bayesian Count Data Modeling

Flexible models for overdispersed counts including negative binomial and zero-inflated distributions.

Implicit Differentiation for Bayesian Inference

Gradient-based methods using implicit function theorem for efficient posterior estimation in complex models.

Bayesian Semi-supervised Learning

Probabilistic approaches leveraging unlabeled data through mixture models and latent class structures.

Meta-analysis Bayesian Framework

Hierarchical models for synthesizing evidence across multiple studies with heterogeneity assessment.

Bayesian Network Topology Discovery

Algorithms for learning directed acyclic graph structures representing conditional dependencies.

Bayesian Tensor Decomposition Methods

Research on Bayesian approaches for multi-way tensor factorization with applications to high-dimensional data analysis and latent factor discovery.

Scalable Variational Bayes for Big Data

Development of stochastic variational inference algorithms that maintain computational tractability while processing massive datasets.

Bayesian Reinforcement Learning Theory

Integration of Bayesian inference with reinforcement learning to quantify uncertainty in exploration-exploitation trade-offs and value function estimation.

Polya Urn Models and Applications

Theoretical and applied studies of exchangeable random processes through Polya urn frameworks with Bayesian interpretations.

Bayesian Topological Data Analysis

Bayesian methods for persistent homology and topological feature extraction from point clouds and complex data structures.

Gradient Flow Variational Inference

Study of optimization dynamics and convergence properties in variational Bayesian inference using continuous gradient flows.

Bayesian Causal Discovery Algorithms

Methods for inferring causal structure from observational data using Bayesian directed acyclic graph models and constraints.

Dirichlet Process Mixture Clustering

Nonparametric Bayesian clustering via Dirichlet process priors with adaptive component selection and inference.

Bayesian Epidemiological Compartmental Models

Bayesian inference for disease transmission dynamics using SEIR and related stochastic compartmental models with parameter uncertainty.

Annealed Importance Sampling Methods

Development and analysis of sequential tempering algorithms for efficient sampling from multimodal posterior distributions.

Bayesian Factor Models for Finance

Application of Bayesian latent factor models to portfolio selection, risk assessment, and asset pricing with time-varying dynamics.

Laplace Approximation Theory Extensions

Theoretical advances in asymptotic normality and refinements of Laplace approximations for posterior inference and marginal likelihood.

Bayesian Multiview Learning Integration

Methods for integrating information across multiple data views or modalities using hierarchical Bayesian frameworks.

Overdispersed Count Data Modeling

Bayesian approaches to modeling count data with excess variation through negative binomial and generalized Poisson distributions.

Gaussian Copula Bayesian Networks

Bayesian inference for high-dimensional continuous distributions using copula-based factorizations and conditional independence structures.

Bayesian Wavelet Shrinkage Methods

Development of Bayesian priors and inference procedures for wavelet coefficients in signal denoising and sparse estimation.

Stochastic Gradient Langevin Dynamics

Analysis of convergence properties and practical implementation of gradient-based sampling for scalable Bayesian inference.

Bayesian Manifold Learning Embedding

Nonparametric Bayesian methods for discovering and modeling latent manifold structure in high-dimensional data spaces.

Indian Buffet Process Models

Infinite latent feature modeling using Indian buffet process priors for discovering sparsely distributed hidden attributes.

Bayesian Network Meta-analysis

Bayesian framework for simultaneously analyzing multiple clinical trials in network structures with treatment comparisons.

Slice Sampling Algorithms Variants

Development and theoretical analysis of auxiliary variable slice sampling techniques for improved MCMC performance.

Bayesian Instrumental Variable Methods

Bayesian approaches to addressing endogeneity and causal effects using instrumental variable models with uncertainty quantification.

Fractional Bayes Factor Computation

Methods for computing and interpreting fractional Bayes factors that reduce prior dependence in model comparison problems.

Bayesian Hawkes Process Estimation

Bayesian inference for self-exciting point processes with applications to earthquake sequences and financial event modeling.

Variational Sparse Coding Methods

Bayesian sparse representation learning through variational inference applied to dictionary learning and signal decomposition.

Bayesian Change Point Detection Methods

Dynamic programming and MCMC methods for detecting abrupt changes in time series with uncertainty quantification.

Cylindrical Data Directional Statistics

Bayesian inference for multivariate directional and cylindrical data with applications to angular-linear relationships.

Bayesian Graph Signal Processing

Methods for inferring signals supported on graph structures using Bayesian wavelets and spectral methods.

Tempered Posterior Distributions

Study of fractional likelihood approaches and temperature-scaled posteriors for robust inference under model misspecification.

Bayesian Ordinal Data Regression

Bayesian methods for ordinal response variables using latent variable frameworks and proportional odds models.

Spectral Methods Bayesian Learning

Integration of spectral methods with Bayesian inference for dimensionality reduction and pattern discovery.

Bayesian Image Segmentation Models

Hierarchical Bayesian approaches to image segmentation using Markov random fields and spatial correlation structures.

Asymptotic Normality Posterior Distribution

Theoretical analysis of when and how posterior distributions achieve asymptotic normality under regularity conditions.

Bayesian Learning to Rank Methods

Bayesian approaches to preference learning and ranking problems with applications to information retrieval systems.

Adaptive Tempering MCMC Schemes

Development of algorithms that adaptively adjust temperature schedules during MCMC sampling for multimodal posteriors.

Bayesian Misspecified Model Analysis

Study of Bayesian inference behavior when the true data generating process differs from model assumptions.

Pitman-Yor Process Generalizations

Theoretical development and applications of Pitman-Yor processes and their extensions for flexible nonparametric modeling.

Bayesian Compositional Data Analysis

Methods for analyzing parts-of-a-whole data using Bayesian log-ratio transformations and simplex constraints.

Variational Message Passing Inference

Distributed variational inference algorithms using message passing on factor graphs for scalable Bayesian computation.

Bayesian Copula Tail Dependence

Inference for extreme dependence structures using Bayesian copula models with focus on asymptotic tail behavior.

Preconditioned Langevin Sampling

Methods for improving mixing and convergence of Langevin-based samplers through adaptive preconditioning strategies.

Bayesian Preference Learning Models

Bayesian inference for learning user preferences from pairwise comparisons and ranking feedback.

Nonstationary Covariance Function Modeling

Bayesian approaches for modeling spatially-varying or temporally-varying covariance structures in stochastic processes.

Bayesian Outlier Detection Robustness

Development of robust Bayesian methods using heavy-tailed distributions for automatic outlier identification and downweighting.

Particle Filter Sequential Inference

Advanced particle filtering techniques for online Bayesian inference in state-space models with degeneracy solutions.

Bayesian Functional Linear Regression

Bayesian inference for regression with functional predictors or responses using basis expansions and smoothness penalties.

Zero-Inflated Model Inference

Bayesian methods for count data with excess zeros using mixture models and latent class structures.

Credibility Theory Bayesian Framework

Application of Bayesian hierarchical models to actuarial credibility and premium estimation problems.

Bayesian Optimal Design Sequences

Sequential adaptive design methods that use Bayesian inference to efficiently allocate experimental resources.

Recursive Importance Sampling Filtering

Recursive algorithms combining importance sampling with filtering for efficient sequential Bayesian inference.

Bayesian Nonparametric Kernel Learning

Research on developing flexible kernel methods within Bayesian frameworks without assuming fixed parametric forms.

Manifold-based Posterior Sampling Techniques

Investigation of sampling algorithms that exploit low-dimensional manifold structure in high-dimensional posterior distributions.

Bayesian Tree Ensemble Methods

Development of probabilistic frameworks combining multiple decision trees with Bayesian uncertainty quantification.

Conditional Independence Bayesian Networks

Study of exploiting conditional independence structures to improve efficiency and interpretability in Bayesian inference.

Spike-and-Slab Prior Structures

Advanced research on mixture priors for variable selection and sparse estimation in high-dimensional problems.

Bayesian Tensor Factorization Methods

Development of Bayesian approaches for decomposing multi-dimensional arrays with uncertainty quantification.

Streaming Data Bayesian Inference

Algorithms for performing exact and approximate Bayesian inference on continuous data streams with memory constraints.

Bayesian Graph Neural Networks

Integration of Bayesian uncertainty principles with graph-based neural architectures for relational data.

Robust Bayesian Loss Functions

Research on developing Bayesian inference methods robust to model misspecification and outliers.

Bayesian Functional Time Series

Methods for analyzing infinite-dimensional functional data evolving through time using Bayesian frameworks.

Approximate Posterior Validation Tests

Diagnostic procedures for assessing quality and reliability of approximate Bayesian inference methods.

Implicit Variational Inference Methods

Development of variational families with implicit distributions for flexible posterior approximation.

Bayesian Transfer Learning Networks

Techniques for leveraging prior knowledge across domains while maintaining uncertainty in Bayesian settings.

Scalable Bayesian Cox Regression

Computational advances for Bayesian semi-parametric survival analysis with large datasets.

Bayesian Anomaly Detection Networks

Probabilistic frameworks for identifying outliers and anomalies with principled uncertainty quantification.

Multimodal Posterior Exploration

Specialized sampling and optimization techniques for posteriors with multiple separated modes.

Bayesian Optimal Experimental Design

Theory and practice of sequential experimental design maximizing information gain under Bayesian uncertainty.

Laplace Approximation Extensions

Advanced techniques extending classical Laplace approximation for better posterior accuracy in complex models.

Bayesian Functional Regression Trees

Recursive partitioning methods for functional responses with Bayesian uncertainty quantification.

Continuous Relaxation Variational Methods

Techniques for approximating discrete latent variable models using continuous relaxations in variational inference.

Bayesian Clustering Uncertainty

Methods for quantifying and propagating uncertainty in cluster assignment and number selection.

Semi-implicit Variational Inference

Hybrid approaches combining implicit and explicit variational distributions for improved flexibility.

Bayesian Conformal Prediction Sets

Integration of Bayesian methods with conformal inference for finite-sample prediction set guarantees.

Differentiable Probabilistic Programming

Development of programming languages enabling automatic differentiation for Bayesian inference algorithms.

Bayesian Change Point Detection

Sequential methods for identifying structural breaks in time series with principled uncertainty bounds.

Parametric Bootstrap Bayesian Methods

Resampling-based approaches combining Bayesian estimation with bootstrap procedures for inference.

Bayesian Online Clustering

Streaming algorithms for discovering and updating cluster structure in real-time data.

Reproducing Kernel Bayesian Methods

Bayesian inference leveraging reproducing kernel Hilbert space theory for flexible function estimation.

Bayesian Extreme Value Theory

Probabilistic methods for modeling tail behavior and rare events using Bayesian frameworks.

Flexible Copula-based Inference

Advanced copula constructions within Bayesian methods for complex dependence structures.

Bayesian Rough Paths Analysis

Incorporation of rough path theory into Bayesian inference for irregular time series data.

Asymptotic Bayesian Decision Theory

Large-sample behavior and optimality properties of Bayesian decision rules.

Scalable Bayesian Item Response Theory

Computational methods for Bayesian latent trait modeling in educational and psychological assessment.

Bayesian Attention Mechanisms

Integration of attention modules with Bayesian uncertainty in neural network architectures.

Nonparametric Bayesian Regression Splines

Flexible smoothing methods combining B-splines with infinite-dimensional priors for function estimation.

Bayesian Demand Forecasting

Predictive methods for commercial demand incorporating uncertainty and hierarchical structures.

Tensor Network Bayesian Models

Probabilistic graphical models exploiting tensor network structures for efficient inference.

Bayesian Symbolic Regression

Probabilistic methods for discovering mathematical equations from data with structural uncertainty.

Covariate-adjusted Bayesian Analysis

Methods for incorporating covariate information in prior specification and posterior estimation.

Bayesian Optimal Power Analysis

Computational approaches for determining sample size and statistical power in Bayesian studies.

Finite Mixture Component Determination

Methods for selecting the number of components in finite mixture models using Bayesian criteria.

Bayesian Process Capability Analysis

Probabilistic assessment of manufacturing process quality and conformance to specifications.

Bayesian Surrogate Model Construction

Emulation of expensive simulators and black-box functions using Bayesian metamodels.

Heteroscedastic Bayesian Linear Models

Regression methods allowing variance to depend on predictors with full Bayesian treatment.

Bayesian Model Criticism Diagnostics

Systematic approaches for assessing model adequacy and identifying sources of misfit.

Approximate Message Passing Inference

Iterative algorithms for approximate posterior computation in high-dimensional sparse problems.

Bayesian Deep Generative Models

Research on integrating Bayesian inference with deep generative architectures like VAEs and GANs for principled uncertainty quantification in generative modeling.

Variational Graph Neural Networks

Development of variational inference techniques for Bayesian learning on graph-structured data and network topology estimation.

Scalable Bayesian Optimization Methods

Advancement of computationally efficient Bayesian optimization algorithms for high-dimensional hyperparameter tuning and expensive function evaluation.

Bayesian Transfer Learning Frameworks

Integration of Bayesian principles with transfer learning to enable domain adaptation and knowledge sharing across related tasks.

Stochastic Variational Inference Convergence

Theoretical analysis and practical development of convergence guarantees for stochastic variational inference on streaming and distributed data.

Posterior Inference for Intractable Likelihoods

Development of inference methods for complex models where likelihood evaluation is computationally intractable or impossible.

Bayesian Change Point Detection Analysis

Methods for identifying structural breaks in time series and sequential data using Bayesian nonparametric and dynamic programming approaches.

Credible Interval Coverage Properties

Theoretical investigation of coverage guarantees and calibration of Bayesian credible intervals under various model assumptions.

Bayesian Instrumental Variable Estimation

Development of Bayesian approaches for causal inference with endogeneity through instrumental variable models and identification strategies.

Cross-Validation for Bayesian Model Selection

Research on Bayesian cross-validation methods as alternatives to marginal likelihood for consistent model comparison and selection.

Bayesian Mixture Regression with Clustering

Methods for simultaneous clustering and regression using finite and infinite mixture models with Bayesian nonparametric priors.

Scalable Gaussian Process Approximations

Development of inducing point methods, spectral approximations, and sparse inference for Gaussian processes on large datasets.

Dirichlet Process Clustering Applications

Application of Dirichlet process mixtures to high-dimensional clustering problems with unknown number of components.

Probabilistic Programming Language Design

Creation and analysis of domain-specific languages for expressing Bayesian models with automatic inference compilation.

Bayesian Anomaly Detection Methods

Development of unsupervised Bayesian approaches for detecting outliers and anomalies in multivariate and sequential data.

Bayesian Hypothesis Testing Alternatives

Development of Bayes factor computation methods and Bayesian significance testing as alternatives to frequentist p-values.

Multi-Task Bayesian Learning Framework

Bayesian hierarchical models for jointly learning multiple related tasks through shared prior structure and information borrowing.

Bayesian Functional Connectivity Analysis

Methods for inferring brain network connections and dynamics from neuroimaging data using Bayesian graphical models.

Variational Inference for Copulas

Scalable variational methods for learning multivariate dependence structures through flexible copula model families.

Bayesian Long Memory Time Series

Inference methods for ARFIMA and other long-memory processes using fractional integration and spectral methods.

Posterior Summary Robustness Analysis

Investigation of how posterior inference robustness changes under perturbations to prior specification and likelihood assumptions.

Bayesian Convolutional Neural Networks

Development of uncertainty quantification methods for convolutional architectures through Bayesian weight distributions.

Bayesian Multivariate Time Series Forecasting

Methods for high-dimensional vector autoregressive models with structured sparsity and forecast density estimation.

Inference for Partially Observed Dynamics

Bayesian methods for state space models and stochastic differential equations with missing observations and measurement error.

Bayesian Clustering with Overlap

Methods for overlapping cluster detection through mixed membership models and Bayesian community detection.

Wasserstein Gradient Flow Inference

Use of optimal transport and Wasserstein distances for posterior approximation and variational inference optimization.

Bayesian Robust Regression Methods

Development of heavy-tailed error distributions and outlier-resistant Bayesian regression for contaminated data.

Scalable Posterior Sampling Algorithms

Development of modern MCMC and SMC samplers designed for distributed computing and massive datasets.

Bayesian Competing Risks Analysis

Methods for survival analysis with multiple event types and dependent censoring using copula-based models.

Zero-Inflated Bayesian Count Regression

Flexible Bayesian models for count data with excessive zeros and variable dispersion patterns.

Posterior Concentration in High Dimensions

Theoretical study of posterior contraction rates and concentration properties in high-dimensional settings with sparsity.

Bayesian Semiparametric Regression Models

Flexible regression methods combining parametric components with nonparametric shape functions using Bayesian splines.

Importance Weighting for Model Comparison

Methods for computing marginal likelihoods and Bayes factors using sequential importance weighting and bridge sampling.

Bayesian Extreme Value Modeling

Development of Bayesian approaches for generalized extreme value distributions and threshold exceedance analysis.

Stratified Sampling for Bayesian Inference

Design of efficient sampling schemes that exploit posterior structure for faster convergence in MCMC.

Bayesian Network Meta-analysis Framework

Bayesian hierarchical models for simultaneous comparison of multiple treatments across diverse studies.

Conditional Independence Screening

Methods for feature selection in high-dimensional Bayesian regression through conditional independence testing.

Bayesian Heterogeneous Treatment Effects

Modeling individualized treatment responses using Bayesian trees, additive models, and causal forests.

Bayesian Tensor Decomposition and Factorization

Development of Bayesian methods for high-dimensional tensor data decomposition with applications to multi-way data analysis and dimensionality reduction.

Scalable Posterior Sampling via Distributed Computing

Research on distributed and parallel algorithms for Bayesian inference that enable efficient posterior sampling across large-scale datasets and computing clusters.

Multivariate Skewed Posterior Approximation

Development of asymmetric variational families for better approximation of multimodal and skewed posteriors.

Bayesian Phase Transition Detection

Statistical methods for identifying abrupt qualitative changes in system behavior in physical and biological systems.

Bayesian Nonparametric Survival Models

Exploration of nonparametric Bayesian approaches for survival analysis including flexible baseline hazard estimation and competing risk frameworks.

Empirical Likelihood Bayesian Hybrid

Combination of empirical likelihood with Bayesian methods for robust inference without parametric assumptions.

Amortized Inference with Neural Networks

Development of neural network-based amortized variational inference methods that learn to approximate posterior distributions across entire parameter spaces.

Bayesian Survival Tree Ensembles

Ensemble methods combining Bayesian tree models for censored survival data with uncertainty quantification.

Bayesian Causal Discovery from Observational Data

Methods for learning directed acyclic graph structures and inferring causal relationships from observational data using Bayesian score-based and constraint-based approaches.

Spectral Methods for Bayesian Inference

Application of Fourier and spectral techniques to accelerate inference for periodic and frequency-domain models.

Stochastic Gradient Langevin Dynamics Convergence

Theoretical analysis and practical refinements of stochastic gradient Langevin algorithms for scalable approximate posterior sampling on mini-batches.

Bayesian Anomaly Detection in Temporal Sequences

Bayesian frameworks for detecting anomalies and changepoints in high-dimensional time series using latent variable models and adaptive priors.

Information Geometric Posterior Divergence

Study of posterior geometry using differential geometry and information divergences for computational optimization.

Manifold-based Variational Inference Methods

Research on variational inference techniques that exploit low-dimensional manifold structure in posterior distributions for improved approximation efficiency.

Bayesian Reinforcement Learning with Posterior Exploration

Integration of Bayesian inference with reinforcement learning to quantify epistemic uncertainty and enable principled exploration-exploitation trade-offs.

Bayesian Optimal Adaptive Design

Sequential experimental design using value of information and dynamic programming for adaptive clinical trials.