{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/bayesian-inference/papers/13","list_of":"/task/bayesian-inference","task":"Bayesian Inference","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":13,"pages_in_order":23,"rows_per_page":100,"rows":[1201,1300],"of":2226,"counts":{"archive_papers_tagged":2226,"with_a_code_link":747,"where_syntology_ran_a_sample":164,"not_listed_spam_title":0,"listed":2226,"listed_where_code_ran":164,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":135,"every_run_a_failure_of_syntologys_instrument":29,"listed_with_a_run_with_no_instrument_failure":135,"listed_every_run_a_failure_of_syntologys_instrument":29,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/bayesian-inference","prev":"/task/bayesian-inference/papers/12","next":"/task/bayesian-inference/papers/14","papers":[{"url":null,"slug":"further-analysis-of-multilevel-stein","title":"Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models","date":"2022-12-06","arxiv_id":"2212.03366","repositories_listed":0,"syntology":null},{"url":null,"slug":"prism-probabilistic-real-time-inference-in","title":"PRISM: Probabilistic Real-Time Inference in Spatial World Models","date":"2022-12-06","arxiv_id":"2212.02988","repositories_listed":0,"syntology":null},{"url":null,"slug":"multielement-polynomial-chaos-kriging-based","title":"Multielement polynomial chaos Kriging-based metamodelling for Bayesian inference of non-smooth systems","date":"2022-12-05","arxiv_id":"2212.02250","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-you-using-test-log-likelihood-correctly","title":"Are you using test log-likelihood correctly?","date":"2022-12-01","arxiv_id":"2212.00219","repositories_listed":0,"syntology":null},{"url":null,"slug":"trust-and-time-preference-measuring-a-causal","title":"Trust and Time Preference: Measuring a Causal Effect in a Random-Assignment Experiment","date":"2022-11-30","arxiv_id":"2211.17080","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-search-and-detection-autonomous-drone","title":"A Search and Detection Autonomous Drone System: from Design to Implementation","date":"2022-11-29","arxiv_id":"2211.15866","repositories_listed":0,"syntology":null},{"url":null,"slug":"double-robust-bayesian-inference-on-average","title":"Double Robust Bayesian Inference on Average Treatment Effects","date":"2022-11-29","arxiv_id":"2211.16298","repositories_listed":0,"syntology":null},{"url":null,"slug":"approximate-gibbs-sampler-for-efficient","title":"Approximate Gibbs Sampler for Efficient Inference of Hierarchical Bayesian Models for Grouped Count Data","date":"2022-11-28","arxiv_id":"2211.15771","repositories_listed":0,"syntology":null},{"url":null,"slug":"looking-at-the-posterior-on-the-origin-of","title":"Looking at the posterior: accuracy and uncertainty of neural-network predictions","date":"2022-11-26","arxiv_id":"2211.14605","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-learning-for-neural-networks-an","title":"Bayesian Learning for Neural Networks: an algorithmic survey","date":"2022-11-21","arxiv_id":"2211.11865","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-non-line-of-sight-imaging-with","title":"Few-shot Non-line-of-sight Imaging with Signal-surface Collaborative Regularization","date":"2022-11-21","arxiv_id":"2211.15367","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-exploration-based-on-information-gain","title":"Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation","date":"2022-11-20","arxiv_id":"2211.10934","repositories_listed":0,"syntology":null},{"url":null,"slug":"orthogonal-polynomials-quadrature-algorithm","title":"Orthogonal Polynomials Approximation Algorithm (OPAA):a functional analytic approach to estimating probability densities","date":"2022-11-16","arxiv_id":"2211.08594","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-approximation-for-bayesian","title":"Understanding Approximation for Bayesian Inference in Neural Networks","date":"2022-11-11","arxiv_id":"2211.06139","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalization-of-generative-model-for","title":"Generalization of generative model for neuronal ensemble inference method","date":"2022-11-07","arxiv_id":"2211.05634","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-averaging-langevin-dynamics-toward","title":"Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms","date":"2022-10-31","arxiv_id":"2211.00100","repositories_listed":0,"syntology":null},{"url":null,"slug":"softbart-soft-bayesian-additive-regression","title":"SoftBart: Soft Bayesian Additive Regression Trees","date":"2022-10-28","arxiv_id":"2210.16375","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-inference-of-transition-matrices","title":"Bayesian Inference of Transition Matrices from Incomplete Graph Data with a Topological Prior","date":"2022-10-27","arxiv_id":"2210.15410","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-methods-in-automated-vehicle-s-car","title":"Bayesian Methods in Automated Vehicle's Car-following Uncertainties: Enabling Strategic Decision Making","date":"2022-10-25","arxiv_id":"2210.13683","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-bayesian-inference-clustering","title":"Variational Bayesian Inference Clustering Based Joint User Activity and Data Detection for Grant-Free Random Access in mMTC","date":"2022-10-25","arxiv_id":"2210.13773","repositories_listed":0,"syntology":null},{"url":null,"slug":"gflowout-dropout-with-generative-flow","title":"GFlowOut: Dropout with Generative Flow Networks","date":"2022-10-24","arxiv_id":"2210.12928","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-latent-structural-causal-models","title":"Learning Latent Structural Causal Models","date":"2022-10-24","arxiv_id":"2210.13583","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-deep-learning-framework-for","title":"Bayesian deep learning framework for uncertainty quantification in high dimensions","date":"2022-10-21","arxiv_id":"2210.11737","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertain-evidence-in-probabilistic-models","title":"Uncertain Evidence in Probabilistic Models and Stochastic Simulators","date":"2022-10-21","arxiv_id":"2210.12236","repositories_listed":0,"syntology":null},{"url":null,"slug":"autoencoded-sparse-bayesian-in-irt","title":"Autoencoded sparse Bayesian in-IRT factorization, calibration, and amortized inference for the Work Disability Functional Assessment Battery","date":"2022-10-20","arxiv_id":"2210.10952","repositories_listed":0,"syntology":null},{"url":null,"slug":"deciphering-the-interaction-of-genetic-and","title":"Bottom-up data integration in polymer models of chromatin organisation","date":"2022-10-20","arxiv_id":"2210.11323","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-representations-of-mean-field-variational","title":"On Representations of Mean-Field Variational Inference","date":"2022-10-20","arxiv_id":"2210.11385","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-aided-laplace-based-bayesian","title":"Deep Learning Aided Laplace Based Bayesian Inference for Epidemiological Systems","date":"2022-10-17","arxiv_id":"2210.08865","repositories_listed":0,"syntology":null},{"url":null,"slug":"marginalized-particle-gibbs-for-multiple","title":"Marginalized particle Gibbs for multiple state-space models coupled through shared parameters","date":"2022-10-13","arxiv_id":"2210.07379","repositories_listed":0,"syntology":null},{"url":null,"slug":"signal-detection-in-mimo-systems-with","title":"Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks","date":"2022-10-08","arxiv_id":"2210.03911","repositories_listed":0,"syntology":null},{"url":null,"slug":"unified-probabilistic-neural-architecture-and","title":"Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness","date":"2022-10-08","arxiv_id":"2210.04083","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-moving-horizon-estimation-for","title":"Robust Bayesian Inference for Moving Horizon Estimation","date":"2022-10-05","arxiv_id":"2210.02166","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-synaptic-failure-enables-sampling","title":"Adaptive Synaptic Failure Enables Sampling from Posterior Predictive Distributions in the Brain","date":"2022-10-04","arxiv_id":"2210.01691","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-wind-park-power-prediction","title":"Probabilistic Wind Park Power Prediction using Bayesian Deep Learning and Generative Adversarial Networks","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-second-law-of-thermodynamics-in","title":"Generalized second law of thermodynamics in the Glosten-Milgrom model","date":"2022-09-30","arxiv_id":"2209.15429","repositories_listed":0,"syntology":null},{"url":null,"slug":"accurate-reliable-and-interpretable","title":"Accurate, reliable and interpretable solubility prediction of druglike molecules with attention pooling and Bayesian learning","date":"2022-09-29","arxiv_id":"2210.07145","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-selection-via-the-intervened","title":"Feature Selection via the Intervened Interpolative Decomposition and its Application in Diversifying Quantitative Strategies","date":"2022-09-29","arxiv_id":"2209.14532","repositories_listed":0,"syntology":null},{"url":null,"slug":"hamiltonian-adaptive-importance-sampling","title":"Hamiltonian Adaptive Importance Sampling","date":"2022-09-27","arxiv_id":"2209.13716","repositories_listed":0,"syntology":null},{"url":null,"slug":"seq2seq-surrogates-of-epidemic-models-to","title":"Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference","date":"2022-09-20","arxiv_id":"2209.09617","repositories_listed":0,"syntology":null},{"url":"/paper/adaptive-dimension-reduction-and-variational","slug":"adaptive-dimension-reduction-and-variational","title":"Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification","date":"2022-09-18","arxiv_id":"2209.08527","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesldm-a-domain-specific-language-for","title":"BayesLDM: A Domain-Specific Language for Probabilistic Modeling of Longitudinal Data","date":"2022-09-12","arxiv_id":"2209.05581","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncovering-regions-of-maximum-dissimilarity","title":"Uncovering Regions of Maximum Dissimilarity on Random Process Data","date":"2022-09-12","arxiv_id":"2209.05569","repositories_listed":0,"syntology":null},{"url":null,"slug":"implicit-full-waveform-inversion-with-deep","title":"Implicit Full Waveform Inversion with Deep Neural Representation","date":"2022-09-08","arxiv_id":"2209.03525","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-gaussian-process-regression","title":"Non-Gaussian Process Regression","date":"2022-09-07","arxiv_id":"2209.03117","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-neural-network-inference-via","title":"Bayesian Neural Network Inference via Implicit Models and the Posterior Predictive Distribution","date":"2022-09-06","arxiv_id":"2209.02188","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-importance-sampling-using-tensor-trains","title":"Deep importance sampling using tensor trains with application to a priori and a posteriori rare event estimation","date":"2022-09-05","arxiv_id":"2209.01941","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-the-impact-of-model","title":"Investigating the Impact of Model Misspecification in Neural Simulation-based Inference","date":"2022-09-05","arxiv_id":"2209.01845","repositories_listed":0,"syntology":null},{"url":null,"slug":"topology-change-aware-data-driven","title":"Topology Change Aware Data-Driven Probabilistic Distribution State Estimation Based on Gaussian Process","date":"2022-09-05","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-inference-for-model-free-and","title":"Variational Inference for Model-Free and Model-Based Reinforcement Learning","date":"2022-09-04","arxiv_id":"2209.01693","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamics-adaptive-continual-reinforcement","title":"Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization","date":"2022-09-01","arxiv_id":"2209.00347","repositories_listed":0,"syntology":null},{"url":null,"slug":"conjugate-natural-selection","title":"Conjugate Natural Selection","date":"2022-08-29","arxiv_id":"2208.13898","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-calibration-of-nonlinear-sensors-with","title":"Dynamic Calibration of Nonlinear Sensors with Time-Drifts and Delays by Bayesian Inference","date":"2022-08-29","arxiv_id":"2208.13819","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-relation-graph-and-graph","title":"Spatial Relation Graph and Graph Convolutional Network for Object Goal Navigation","date":"2022-08-27","arxiv_id":"2208.13031","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-and-compositionality-a-unification","title":"Learning and Compositionality: a Unification Attempt via Connectionist Probabilistic Programming","date":"2022-08-26","arxiv_id":"2208.12789","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-framework-for-geodesics-under","title":"A deep learning framework for geodesics under spherical Wasserstein-Fisher-Rao metric and its application for weighted sample generation","date":"2022-08-25","arxiv_id":"2208.12145","repositories_listed":0,"syntology":null},{"url":null,"slug":"simulating-how-animals-learn-a-new-modelling","title":"Simulating how animals learn: a new modelling framework applied to the process of optimal foraging","date":"2022-08-25","arxiv_id":"2208.12305","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-bayesian-nonnegative-matrix","title":"Robust Bayesian Nonnegative Matrix Factorization with Implicit Regularizers","date":"2022-08-22","arxiv_id":"2208.10053","repositories_listed":0,"syntology":null},{"url":null,"slug":"scale-invariant-process-regression","title":"Scale invariant process regression: Towards Bayesian ML with minimal assumptions","date":"2022-08-22","arxiv_id":"2208.10461","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-resource-allocation-for-anti-jamming","title":"A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach","date":"2022-08-10","arxiv_id":"2208.05269","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-maxout-network-gaussian-process","title":"Deep Maxout Network Gaussian Process","date":"2022-08-08","arxiv_id":"2208.04468","repositories_listed":0,"syntology":null},{"url":null,"slug":"swiss-a-scalable-markov-chain-monte-carlo","title":"SwISS: A Scalable Markov chain Monte Carlo Divide-and-Conquer Strategy","date":"2022-08-08","arxiv_id":"2208.04080","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-rates-for-regularized-conditional","title":"Optimal Rates for Regularized Conditional Mean Embedding Learning","date":"2022-08-02","arxiv_id":"2208.01711","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-gradient-based-mcmc-in-discrete","title":"Enhanced gradient-based MCMC in discrete spaces","date":"2022-07-29","arxiv_id":"2208.00040","repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-and-computational-trade-offs-in-1","title":"Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection","date":"2022-07-22","arxiv_id":"2207.11208","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-particle-based-variational","title":"A Two-stage Multiband WiFi Sensing Scheme via Stochastic Particle-Based Variational Bayesian Inference","date":"2022-07-21","arxiv_id":"2207.10427","repositories_listed":0,"syntology":null},{"url":null,"slug":"mean-field-variational-inference-via","title":"Mean-field Variational Inference via Wasserstein Gradient Flow","date":"2022-07-17","arxiv_id":"2207.08074","repositories_listed":0,"syntology":null},{"url":null,"slug":"minimum-description-length-control","title":"Minimum Description Length Control","date":"2022-07-17","arxiv_id":"2207.08258","repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-variable-models-for-bayesian-causal","title":"Latent Variable Models for Bayesian Causal Discovery","date":"2022-07-12","arxiv_id":"2207.05723","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-posterior-estimation-with","title":"Neural Posterior Estimation with Differentiable Simulators","date":"2022-07-12","arxiv_id":"2207.05636","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparative-study-of-inference-methods-for-1","title":"Comparative Study of Inference Methods for Interpolative Decomposition","date":"2022-06-29","arxiv_id":"2206.14542","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-neural-network-detector-for-an","title":"Bayesian Neural Network Detector for an Orthogonal Time Frequency Space Modulation","date":"2022-06-27","arxiv_id":"2206.13235","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-unifying-perceptual-reasoning-and","title":"Towards Unifying Perceptual Reasoning and Logical Reasoning","date":"2022-06-27","arxiv_id":"2206.13174","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-bayesian-inference-for-cp-tensor","title":"Variational Bayesian inference for CP tensor completion with side information","date":"2022-06-24","arxiv_id":"2206.12486","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-model-calibration-for-block","title":"Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport","date":"2022-06-22","arxiv_id":"2206.11343","repositories_listed":0,"syntology":null},{"url":null,"slug":"cold-posteriors-through-pac-bayes","title":"Cold Posteriors through PAC-Bayes","date":"2022-06-22","arxiv_id":"2206.11173","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiband-delay-estimation-for-localization","title":"Multiband Delay Estimation for Localization Using a Two-Stage Global Estimation Scheme","date":"2022-06-20","arxiv_id":"2206.09751","repositories_listed":0,"syntology":null},{"url":null,"slug":"faster-sampling-from-log-concave","title":"Sampling from Log-Concave Distributions over Polytopes via a Soft-Threshold Dikin Walk","date":"2022-06-19","arxiv_id":"2206.09384","repositories_listed":0,"syntology":null},{"url":null,"slug":"scaling-multi-species-occupancy-models-to","title":"Scaling multi-species occupancy models to large citizen science datasets","date":"2022-06-17","arxiv_id":"2206.08894","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-aware-evaluation-of-time-series","title":"Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift","date":"2022-06-17","arxiv_id":"2206.08640","repositories_listed":0,"syntology":null},{"url":null,"slug":"calibrating-agent-based-models-to-microdata","title":"Calibrating Agent-based Models to Microdata with Graph Neural Networks","date":"2022-06-15","arxiv_id":"2206.07570","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-adults-understand-what-young-children-say","title":"How Adults Understand What Young Children Say","date":"2022-06-15","arxiv_id":"2206.07807","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-bayes-deep-operator-network-a","title":"Variational Bayes Deep Operator Network: A data-driven Bayesian solver for parametric differential equations","date":"2022-06-12","arxiv_id":"2206.05655","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-safe-use-of-prior-densities-for","title":"On the safe use of prior densities for Bayesian model selection","date":"2022-06-10","arxiv_id":"2206.05210","repositories_listed":0,"syntology":null},{"url":null,"slug":"pavi-plate-amortized-variational-inference","title":"PAVI: Plate-Amortized Variational Inference","date":"2022-06-10","arxiv_id":"2206.05111","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-learning-and-scenario-based-mpc-design-for","title":"A Learning- and Scenario-based MPC Design for Nonlinear Systems in LPV Framework with Safety and Stability Guarantees","date":"2022-06-06","arxiv_id":"2206.02880","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-circuit-sizing-with-multi-objective","title":"Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference","date":"2022-06-06","arxiv_id":"2206.02391","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-intrinsic-groupwise-registration-via","title":"BInGo: Bayesian Intrinsic Groupwise Registration via Explicit Hierarchical Disentanglement","date":"2022-06-06","arxiv_id":"2206.02377","repositories_listed":0,"syntology":null},{"url":null,"slug":"information-threshold-bayesian-inference-and","title":"Information Threshold, Bayesian Inference and Decision-Making","date":"2022-06-05","arxiv_id":"2206.02266","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-inference-of-stochastic-dynamical","title":"Bayesian Inference of Stochastic Dynamical Networks","date":"2022-06-02","arxiv_id":"2206.00858","repositories_listed":0,"syntology":null},{"url":null,"slug":"excess-risk-analysis-for-epistemic","title":"Excess risk analysis for epistemic uncertainty with application to variational inference","date":"2022-06-02","arxiv_id":"2206.01606","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-inference-for-the-multinomial-probit","title":"Bayesian Inference for the Multinomial Probit Model under Gaussian Prior Distribution","date":"2022-06-01","arxiv_id":"2206.00720","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-active-learning-for-scanning-probe","title":"Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning","date":"2022-05-30","arxiv_id":"2205.15458","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-low-rank-interpolative-decomposition","title":"Bayesian Low-Rank Interpolative Decomposition for Complex Datasets","date":"2022-05-30","arxiv_id":"2205.14825","repositories_listed":0,"syntology":null},{"url":null,"slug":"deterministic-langevin-monte-carlo-with","title":"Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference","date":"2022-05-27","arxiv_id":"2205.14240","repositories_listed":0,"syntology":null},{"url":null,"slug":"analytics-of-business-time-series-using","title":"Analytics of Business Time Series Using Machine Learning and Bayesian Inference","date":"2022-05-25","arxiv_id":"2205.12905","repositories_listed":0,"syntology":null},{"url":null,"slug":"flexible-and-hierarchical-prior-for-bayesian","title":"Flexible and Hierarchical Prior for Bayesian Nonnegative Matrix Factorization","date":"2022-05-23","arxiv_id":"2205.11025","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameters-identification-for-an-inverse","title":"Parameters identification for an inverse problem arising from a binary option using a Bayesian inference approach","date":"2022-05-23","arxiv_id":"2205.11012","repositories_listed":0,"syntology":null},{"url":null,"slug":"quasi-black-box-variational-inference-with","title":"Quasi Black-Box Variational Inference with Natural Gradients for Bayesian Learning","date":"2022-05-23","arxiv_id":"2205.11568","repositories_listed":0,"syntology":null},{"url":null,"slug":"rl-with-kl-penalties-is-better-viewed-as","title":"RL with KL penalties is better viewed as Bayesian inference","date":"2022-05-23","arxiv_id":"2205.11275","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-inference-for-bayesian-bridge","title":"Variational Inference for Bayesian Bridge Regression","date":"2022-05-19","arxiv_id":"2205.09515","repositories_listed":0,"syntology":null}],"record_sha256":"8306d0c4ba74fdaec07c0a7e51a513f025e1a4ca3f034d6fc1059058c865ab2e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}