{"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/probabilistic-programming/papers/2","list_of":"/task/probabilistic-programming","task":"Probabilistic Programming","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":2,"pages_in_order":3,"rows_per_page":100,"rows":[101,200],"of":273,"counts":{"archive_papers_tagged":273,"with_a_code_link":92,"where_syntology_ran_a_sample":19,"not_listed_spam_title":0,"listed":273,"listed_where_code_ran":19,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":15,"every_run_a_failure_of_syntologys_instrument":4,"listed_with_a_run_with_no_instrument_failure":15,"listed_every_run_a_failure_of_syntologys_instrument":4,"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/probabilistic-programming","prev":"/task/probabilistic-programming","next":"/task/probabilistic-programming/papers/3","papers":[{"url":null,"slug":"probabilistic-parameter-estimators-and","title":"Probabilistic Parameter Estimators and Calibration Metrics for Pose Estimation from Image Features","date":"2024-07-23","arxiv_id":"2407.16223","repositories_listed":0,"syntology":null},{"url":null,"slug":"querying-labeled-time-series-data-with","title":"Querying Labeled Time Series Data with Scenario Programs","date":"2024-06-25","arxiv_id":"2406.17627","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-programming-with-programmable","title":"Probabilistic Programming with Programmable Variational Inference","date":"2024-06-22","arxiv_id":"2406.15742","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-petri-nets-meet-probabilistic","title":"Data Petri Nets meet Probabilistic Programming (Extended version)","date":"2024-06-12","arxiv_id":"2406.11883","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-probabilistic-scenario-programs","title":"ScenicNL: Generating Probabilistic Scenario Programs from Natural Language","date":"2024-05-03","arxiv_id":"2405.03709","repositories_listed":0,"syntology":null},{"url":null,"slug":"physics-based-causal-reasoning-for-safe","title":"COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty","date":"2024-03-21","arxiv_id":"2403.14488","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-efficient-estimation-using-monte","title":"Automated Efficient Estimation using Monte Carlo Efficient Influence Functions","date":"2024-02-29","arxiv_id":"2403.00158","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-incremental-belief-updates-using","title":"Efficient Incremental Belief Updates Using Weighted Virtual Observations","date":"2024-02-10","arxiv_id":"2402.06940","repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-learning-of-conjunction-data","title":"Statistical Learning of Conjunction Data Messages Through a Bayesian Non-Homogeneous Poisson Process","date":"2023-11-09","arxiv_id":"2311.05426","repositories_listed":0,"syntology":null},{"url":null,"slug":"worst-case-analysis-is-maximum-a-posteriori","title":"Worst-Case Analysis is Maximum-A-Posteriori Estimation","date":"2023-10-15","arxiv_id":"2310.09774","repositories_listed":0,"syntology":null},{"url":null,"slug":"inferring-capabilities-from-task-performance","title":"Inferring Capabilities from Task Performance with Bayesian Triangulation","date":"2023-09-21","arxiv_id":"2309.11975","repositories_listed":0,"syntology":null},{"url":null,"slug":"pearl-s-and-jeffrey-s-update-as-modes-of","title":"Pearl's and Jeffrey's Update as Modes of Learning in Probabilistic Programming","date":"2023-09-13","arxiv_id":"2309.07053","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-probabilistic-programming-to-complexity","title":"From Probabilistic Programming to Complexity-based Programming","date":"2023-07-28","arxiv_id":"2307.15453","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-an-architectural-framework-for","title":"Towards an architectural framework for intelligent virtual agents using probabilistic programming","date":"2023-07-20","arxiv_id":"2307.10693","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-heavy-tailed-algebra-for-probabilistic","title":"A Heavy-Tailed Algebra for Probabilistic Programming","date":"2023-06-15","arxiv_id":"2306.09262","repositories_listed":0,"syntology":null},{"url":null,"slug":"string-diagrams-with-factorized-densities","title":"String Diagrams with Factorized Densities","date":"2023-05-04","arxiv_id":"2305.02506","repositories_listed":0,"syntology":null},{"url":null,"slug":"dimensionality-reduction-as-probabilistic","title":"Dimensionality Reduction as Probabilistic Inference","date":"2023-04-15","arxiv_id":"2304.07658","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-relations-for-modelling","title":"Probabilistic unifying relations for modelling epistemic and aleatoric uncertainty: semantics and automated reasoning with theorem proving","date":"2023-03-16","arxiv_id":"2303.09692","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-probabilistic-logic-programming-in","title":"Neural Probabilistic Logic Programming in Discrete-Continuous Domains","date":"2023-03-08","arxiv_id":"2303.04660","repositories_listed":0,"syntology":null},{"url":null,"slug":"declarative-probabilistic-logic-programming","title":"Declarative Probabilistic Logic Programming in Discrete-Continuous Domains","date":"2023-02-21","arxiv_id":"2302.10674","repositories_listed":0,"syntology":null},{"url":null,"slug":"o-pap-spaces-reasoning-denotationally-about","title":"$ω$PAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs","date":"2023-02-21","arxiv_id":"2302.10636","repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-expert-opinion-on-observable","title":"Incorporating Expert Opinion on Observable Quantities into Statistical Models -- A General Framework","date":"2023-02-10","arxiv_id":"2302.06391","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-and-correct-gradient-based-optimisation","title":"Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing","date":"2023-01-09","arxiv_id":"2301.03415","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-bioprocess-engineering-meets-machine","title":"When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development","date":"2022-09-02","arxiv_id":"2209.01083","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":"multi-model-probabilistic-programming","title":"Multi-Model Probabilistic Programming","date":"2022-08-12","arxiv_id":"2208.06329","repositories_listed":0,"syntology":null},{"url":null,"slug":"proceedings-38th-international-conference-on","title":"Proceedings 38th International Conference on Logic Programming","date":"2022-08-04","arxiv_id":"2208.02685","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-plug-n-play-task-level-autonomy-for","title":"Towards Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Generative Models","date":"2022-07-20","arxiv_id":"2207.09713","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-program-optimization-with","title":"Tensor Program Optimization with Probabilistic Programs","date":"2022-05-26","arxiv_id":"2205.13603","repositories_listed":0,"syntology":null},{"url":null,"slug":"designing-perceptual-puzzles-by","title":"Designing Perceptual Puzzles by Differentiating Probabilistic Programs","date":"2022-04-26","arxiv_id":"2204.12301","repositories_listed":0,"syntology":null},{"url":null,"slug":"program-analysis-of-probabilistic-programs","title":"Program Analysis of Probabilistic Programs","date":"2022-04-14","arxiv_id":"2204.06868","repositories_listed":0,"syntology":null},{"url":null,"slug":"guaranteed-bounds-for-posterior-inference-in","title":"Guaranteed Bounds for Posterior Inference in Universal Probabilistic Programming","date":"2022-04-06","arxiv_id":"2204.02948","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-meta-probabilistic-programming-language-for","title":"A meta-probabilistic-programming language for bisimulation of probabilistic and non-well-founded type systems","date":"2022-03-30","arxiv_id":"2203.15970","repositories_listed":0,"syntology":null},{"url":null,"slug":"higher-order-generalization-bounds-learning","title":"Higher-Order Generalization Bounds: Learning Deep Probabilistic Programs via PAC-Bayes Objectives","date":"2022-03-30","arxiv_id":"2203.15972","repositories_listed":0,"syntology":null},{"url":null,"slug":"compartmental-models-for-covid-19-and-control","title":"Compartmental Models for COVID-19 and Control via Policy Interventions","date":"2022-03-06","arxiv_id":"2203.02860","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-probabilistic-programming-idiom-for-active","title":"A Probabilistic Programming Idiom for Active Knowledge Search","date":"2022-02-19","arxiv_id":"2202.09555","repositories_listed":0,"syntology":null},{"url":null,"slug":"weighted-programming","title":"Weighted Programming","date":"2022-02-15","arxiv_id":"2202.07577","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixed-nondeterministic-probabilistic-automata","title":"Mixed Nondeterministic-Probabilistic Automata: Blending graphical probabilistic models with nondeterminism","date":"2022-01-19","arxiv_id":"2201.07474","repositories_listed":0,"syntology":null},{"url":null,"slug":"surrogate-likelihoods-for-variational","title":"Surrogate Likelihoods for Variational Annealed Importance Sampling","date":"2021-12-22","arxiv_id":"2112.12194","repositories_listed":0,"syntology":null},{"url":null,"slug":"simulation-intelligence-towards-a-new","title":"Simulation Intelligence: Towards a New Generation of Scientific Methods","date":"2021-12-06","arxiv_id":"2112.03235","repositories_listed":0,"syntology":null},{"url":null,"slug":"querying-labelled-data-with-scenario-programs","title":"Querying Labelled Data with Scenario Programs for Sim-to-Real Validation","date":"2021-12-01","arxiv_id":"2112.00206","repositories_listed":0,"syntology":null},{"url":null,"slug":"testing-probabilistic-circuits","title":"Testing Probabilistic Circuits","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mapping-probability-word-problems-to","title":"Mapping probability word problems to executable representations","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-scenario-based-platform-for-testing","title":"A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation","date":"2021-10-28","arxiv_id":"2110.14870","repositories_listed":0,"syntology":null},{"url":null,"slug":"flip-hoisting-exploiting-repeated-parameters","title":"flip-hoisting: Exploiting Repeated Parameters in Discrete Probabilistic Programs","date":"2021-10-19","arxiv_id":"2110.10284","repositories_listed":0,"syntology":null},{"url":null,"slug":"lazyppl-laziness-and-types-in-non-parametric","title":"LazyPPL: laziness and types in non-parametric probabilistic programs","date":"2021-10-08","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"slash-embracing-probabilistic-circuits-into","title":"SLASH: Embracing Probabilistic Circuits into Neural Answer Set Programming","date":"2021-10-07","arxiv_id":"2110.03395","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-and-quantifying-malicious-activity","title":"Detecting and Quantifying Malicious Activity with Simulation-based Inference","date":"2021-10-06","arxiv_id":"2110.02483","repositories_listed":0,"syntology":null},{"url":null,"slug":"smproblog-stable-model-semantics-in-problog","title":"SMProbLog: Stable Model Semantics in ProbLog and its Applications in Argumentation","date":"2021-10-05","arxiv_id":"2110.01990","repositories_listed":0,"syntology":null},{"url":null,"slug":"unifying-ai-algorithms-with-probabilistic","title":"Unifying AI Algorithms with Probabilistic Programming using Implicitly Defined Representations","date":"2021-10-05","arxiv_id":"2110.02325","repositories_listed":0,"syntology":null},{"url":null,"slug":"einsteinvi-general-and-integrated-stein","title":"EinSteinVI: General and Integrated Stein Variational Inference","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-processes-to-speed-up-mcmc-with","title":"Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect","date":"2021-09-28","arxiv_id":"2109.13891","repositories_listed":0,"syntology":null},{"url":null,"slug":"proceedings-37th-international-conference-on","title":"Proceedings 37th International Conference on Logic Programming (Technical Communications)","date":"2021-09-15","arxiv_id":"2109.07914","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-the-ieee-av-test-challenge-with","title":"Addressing the IEEE AV Test Challenge with Scenic and VerifAI","date":"2021-08-20","arxiv_id":"2108.13796","repositories_listed":0,"syntology":null},{"url":null,"slug":"bob-and-alice-go-to-a-bar-reasoning-about","title":"Bob and Alice Go to a Bar: Reasoning About Future With Probabilistic Programs","date":"2021-08-09","arxiv_id":"2108.03834","repositories_listed":0,"syntology":null},{"url":null,"slug":"pixyz-a-library-for-developing-deep","title":"Pixyz: a Python library for developing deep generative models","date":"2021-07-28","arxiv_id":"2107.13109","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-bayesian-specification-inference","title":"Supervised Bayesian Specification Inference from Demonstrations","date":"2021-07-06","arxiv_id":"2107.02912","repositories_listed":0,"syntology":null},{"url":null,"slug":"expectation-programming","title":"Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently","date":"2021-06-09","arxiv_id":"2106.04953","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-train-your-program","title":"How To Train Your Program: a Probabilistic Programming Pattern for Bayesian Learning From Data","date":"2021-05-08","arxiv_id":"2105.03650","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-programming-bots-in-intuitive","title":"Probabilistic Programming Bots in Intuitive Physics Game Play","date":"2021-04-05","arxiv_id":"2104.01980","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-an-inference-algorithm-for","title":"Meta-Learning an Inference Algorithm for Probabilistic Programs","date":"2021-03-01","arxiv_id":"2103.00737","repositories_listed":0,"syntology":null},{"url":null,"slug":"einstein-vi-general-and-integrated-stein","title":"Einstein VI: General and Integrated Stein Variational Inference in NumPyro","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"paraconsistent-foundations-for-probabilistic","title":"Paraconsistent Foundations for Probabilistic Reasoning, Programming and Concept Formation","date":"2020-12-28","arxiv_id":"2012.14474","repositories_listed":0,"syntology":null},{"url":null,"slug":"complex-coordinate-based-meta-analysis-with","title":"Complex Coordinate-Based Meta-Analysis with Probabilistic Programming","date":"2020-12-02","arxiv_id":"2012.01303","repositories_listed":0,"syntology":null},{"url":null,"slug":"transforming-worlds-automated-involutive-mcmc","title":"Transforming Worlds: Automated Involutive MCMC for Open-Universe Probabilistic Models","date":"2020-11-23","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"survival-prediction-and-risk-estimation-of","title":"Survival prediction and risk estimation of Glioma patients using mRNA expressions","date":"2020-11-02","arxiv_id":"2011.00659","repositories_listed":0,"syntology":null},{"url":null,"slug":"recalibrating-classifiers-for-interpretable","title":"Recalibrating classifiers for interpretable abusive content detection","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"baycann-streamlining-bayesian-calibration","title":"BayCANN: Streamlining Bayesian Calibration with Artificial Neural Network Metamodeling","date":"2020-10-26","arxiv_id":"2010.13452","repositories_listed":0,"syntology":null},{"url":null,"slug":"simulation-based-inference-methods-for","title":"Simulation-based inference methods for particle physics","date":"2020-10-13","arxiv_id":"2010.06439","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-policy-search-for-stochastic-domains","title":"Bayesian Policy Search for Stochastic Domains","date":"2020-10-01","arxiv_id":"2010.00284","repositories_listed":0,"syntology":null},{"url":null,"slug":"neuro-symbolic-neurodegenerative-disease","title":"Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels","date":"2020-09-16","arxiv_id":"2009.07738","repositories_listed":0,"syntology":null},{"url":null,"slug":"transforming-probabilistic-programs-for-model","title":"Transforming Probabilistic Programs for Model Checking","date":"2020-08-21","arxiv_id":"2008.09680","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-analysis-in-spect-reconstruction","title":"Uncertainty Analysis in SPECT Reconstruction based on Probabilistic Programming","date":"2020-08-19","arxiv_id":"2008.08230","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-programmatic-and-semantic-approach-to-1","title":"A Programmatic and Semantic Approach to Explaining and Debugging Neural Network Based Object Detectors","date":"2020-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"formal-analysis-and-redesign-of-a-neural","title":"Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI","date":"2020-05-14","arxiv_id":"2005.07173","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastically-differentiable-probabilistic","title":"Stochastically Differentiable Probabilistic Programs","date":"2020-03-02","arxiv_id":"2003.00704","repositories_listed":0,"syntology":null},{"url":null,"slug":"struct-mmsb-mixed-membership-stochastic","title":"Struct-MMSB: Mixed Membership Stochastic Blockmodels with Interpretable Structured Priors","date":"2020-02-21","arxiv_id":"2002.09523","repositories_listed":0,"syntology":null},{"url":null,"slug":"tfpmcmc-modern-markov-chain-monte-carlo-tools","title":"tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware","date":"2020-02-04","arxiv_id":"2002.01184","repositories_listed":0,"syntology":null},{"url":null,"slug":"sampling-prediction-matching-examples-in","title":"Sampling Prediction-Matching Examples in Neural Networks: A Probabilistic Programming Approach","date":"2020-01-09","arxiv_id":"2001.03076","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-programmatic-and-semantic-approach-to","title":"A Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors","date":"2019-12-01","arxiv_id":"1912.00289","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-causal-inference-via-probabilistic","title":"Bayesian causal inference via probabilistic program synthesis","date":"2019-10-30","arxiv_id":"1910.14124","repositories_listed":0,"syntology":null},{"url":null,"slug":"191013324","title":"Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support","date":"2019-10-29","arxiv_id":"1910.13324","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-probabilistic-surrogate-networks-for","title":"Probabilistic Surrogate Networks for Simulators with Unbounded Randomness","date":"2019-10-25","arxiv_id":"1910.11950","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-inference-amortization-in-graphical","title":"Efficient Inference Amortization in Graphical Models using Structured Continuous Conditional Normalizing Flows","date":"2019-10-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-tracking-in-dynamic-probabilistic","title":"Graph Tracking in Dynamic Probabilistic Programs via Source Transformations","date":"2019-10-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"universal-marginaliser-for-deep-amortised","title":"Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs","date":"2019-10-16","arxiv_id":"1910.07474","repositories_listed":0,"syntology":null},{"url":null,"slug":"static-analysis-for-probabilistic-programs","title":"Static Analysis for Probabilistic Programs","date":"2019-09-10","arxiv_id":"1909.05076","repositories_listed":0,"syntology":null},{"url":null,"slug":"strengthening-the-case-for-a-bayesian","title":"Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming","date":"2019-08-07","arxiv_id":"1908.02427","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-synthesis-of-probabilistic-programs","title":"Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling","date":"2019-07-14","arxiv_id":"1907.06249","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-deep-learning-with-hierarchical","title":"Bayesian deep learning with hierarchical prior: Predictions from limited and noisy data","date":"2019-07-08","arxiv_id":"1907.04240","repositories_listed":0,"syntology":null},{"url":null,"slug":"deployable-probabilistic-programming","title":"Deployable probabilistic programming","date":"2019-06-20","arxiv_id":"1906.11199","repositories_listed":0,"syntology":null},{"url":null,"slug":"hijacking-malaria-simulators-with","title":"Hijacking Malaria Simulators with Probabilistic Programming","date":"2019-05-29","arxiv_id":"1905.12432","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-random-conditional-distribution-for","title":"The Random Conditional Distribution for Higher-Order Probabilistic Inference","date":"2019-03-25","arxiv_id":"1903.10556","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-search-based-weighted-model","title":"Efficient Search-Based Weighted Model Integration","date":"2019-03-13","arxiv_id":"1903.05334","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-variable-elimination-for-plated-factor","title":"Tensor Variable Elimination for Plated Factor Graphs","date":"2019-02-08","arxiv_id":"1902.03210","repositories_listed":0,"syntology":null},{"url":null,"slug":"doubly-bayesian-optimization","title":"Doubly Bayesian Optimization","date":"2018-12-11","arxiv_id":"1812.04562","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-theory-of-mind-for-autonomous-agents","title":"Nested Reasoning About Autonomous Agents Using Probabilistic Programs","date":"2018-12-04","arxiv_id":"1812.01569","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-inference-of-temporal-task","title":"Bayesian Inference of Temporal Task Specifications from Demonstrations","date":"2018-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-mapping-and-calibration-via","title":"Joint Mapping and Calibration via Differentiable Sensor Fusion","date":"2018-11-21","arxiv_id":"1812.00880","repositories_listed":0,"syntology":null},{"url":null,"slug":"effect-handling-for-composable-program","title":"Effect Handling for Composable Program Transformations in Edward2","date":"2018-11-15","arxiv_id":"1811.06150","repositories_listed":0,"syntology":null}],"record_sha256":"c7a2a36461a5c1e75660a0427177f15cbefd7be43be1f6acad24ca316444a19a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}