Papers Bayesian Inference
“Bayesian Inference” 태그가 달린 논문 2,612편 · 필터 해제
On the approximation of posterior laws in compound loss models by conditional Wasserstein GANs
Bayesian inference in compound loss models must often be repeated across policies, market scenarios, and prior specifications. Outside conjugate cases, this may require repeated numerical integration or Markov chain Mont…
Bayesian InferenceSpike-based Belief Propagation in Nonlinear Dynamical Systems
This paper presents a Bayesian control framework that integrates spike-based dynamics with probabilistic inference for adaptive control. Bayesian inference is widely regarded as a core computational principle of brain fu…
Bayesian InferenceScalable Amortized Variational Inference for Non-Poisson Buy-'Til-You-Die Models
Despite the wide variety of existing Buy-`Til-You-Die (BTYD) models, nearly all rely upon the convenient assumption of transactions following a Poisson process. As modern customer bases grow larger and more diverse, a ma…
Bayesian InferenceHeteroscedastic Neural Surrogate Modeling for Robust and Rapid Bayesian Inference in Fusion Plasma Diagnostics
Bayesian inference via Markov Chain Monte Carlo (MCMC) provides effective parameter estimation, but its real-time application in complex physical systems is hindered by heavy computational bottlenecks and extreme sensiti…
Bayesian InferenceKnowing When to Stop: Bayesian Optimal Stopping for LLM Evaluations
LLM evaluations often use fixed sampling budgets, testing every item the same number of times even after estimates are precise. We introduce optstop, a precision-based adaptive stopping framework that treats evaluation a…
Bayesian InferenceA Hybrid Nested Harness for Decoupling Structure and Parameters in LLM-Driven Optimization
In evolutionary algorithms powered by language models, the LLM acts as a single operator that simultaneously updates structural components (like control flow) and continuous parameters. While LLMs can be good at the firs…
Bayesian InferenceDivide-and-Conquer: Towards Generalizable Amortized Bayesian Inference for the Drift Diffusion Model
The drift diffusion model (DDM) is a cornerstone of cognitive decision-making research. Although numerous estimation methods exist, researchers continue to seek inference approaches that are both fast and flexible across…
Bayesian InferenceLeveraging System-Level Observations to Inform Bayesian Learning of Model Parameters for Quantitative Verification
Combining Bayesian learning and quantitative verification is a powerful toolset for analysing key quantitative properties of software systems, like reliability and response time. However, the accuracy and robustness of v…
Bayesian InferenceRecursive Gaussian Processes and the Bayesian Brain
Predictive coding offers a powerful framework for cortical computation, yet scalable implementations that respect both Bayesian exactness and neurobiological constraints remain scarce. We bridge this gap by formally conn…
Gaussian ProcessesBayesian InferenceGeneralised Robust Bayes for Joint Inference of Model and Contamination
Generalised Bayesian inference (GBI) has emerged as a compelling robust alternative to standard Bayesian inference, mitigating sensitivity to data contamination by replacing the log-likelihood with a robust loss or diver…
Bayesian InferenceOutlier DetectionInverse Bayesian Inference for Extracting Lesion Dynamics from Longitudinal Spectral CT
Longitudinal medical imaging captures temporal evolution of lesions, yet extracting the underlying dynamical parameters governing this evolution remains challenging. We propose an inverse Bayesian framework for inferring…
Bayesian InferenceVerbalized Particle Posterior: Bayesian Inference over Natural Language Hypotheses
Verbalized Machine Learning (VML) parameterizes a model as a natural-language prompt that an LLM evaluates as f(x; theta). The framework is interpretable, but it commits to a single hypothesis with no measure of uncertai…
Bayesian InferenceAmortized Bayesian Causal Discovery of Extended Factor Graphs
Learning causal graphs from interventional data is a challenging problem with broad applications. In molecular biology, for example, a central goal is to uncover gene regulatory networks from large-scale perturbation dat…
Bayesian InferenceDecision Making Needs Uncertainty Quantification [Lecture Notes]
Many signal processing systems ultimately exist to {act}. Whenever the state variable that determines the action to be taken by a decision maker, or agent, is uncertain, the way that uncertainty is represented decides ho…
Bayesian InferenceDecision MakingRobot Trajectron V3: A Probabilistic Shared Control Framework for SE(3) Manipulation
We aim to address the challenge of teleoperating robotic arms for high-degree-of-freedom (high-DoF) manipulation tasks, which is cognitively demanding and error-prone, particularly when relying on low-bandwidth interface…
Trajectory PredictionBayesian InferencePoint CloudsHypergraph Neural Stochastic Diffusion: An SDE Framework for Uncertainty Estimation
Hypergraph neural networks have shown powerful capability in modeling higher-order relations, yet their predictive uncertainty remains underexplored. Unlike pairwise graphs, uncertainty in hypergraphs arises not only fro…
Representation LearningBayesian InferenceEfficient Bayesian Deep Ensembles via Analytic Predictive Inference
We introduce an efficient Bayesian deep ensemble method for predictive regression designed to enhance interpretability while maintaining competitive predictive performance and computational efficiency. Our method combine…
Computational EfficiencyBayesian InferenceGeometric Causal Models
Scientists often seek to draw causal inferences from structured data that is not independently and identically distributed, such as spatial data, network data, or molecular data. We develop geometric causal models (GCMs)…
Bayesian InferenceCausal InferenceGeometry-Aware Bayesian Quantification via Compositional Data Analysis
Accurately estimating the unknown target label distribution is the critical first step for adapting to label shift. This task, widely known as quantification or class prevalence estimation, has recently seen significant …
Bayesian InferenceIntegrating Neural Encoders in Bayesian Generalized Linear Mixed Models for Multimodal Data
Scalable Bayesian inference for generalized linear mixed models (GLMMs) provides uncertainty-aware analysis of correlated longitudinal data, but existing scalable approaches largely assume low-dimensional tabular predict…
Representation LearningBayesian Inference