Towards formal verification of Bayesian inference in probabilistic programming via guaranteed bounds

Towards formal verification of Bayesian inference in probabilistic programming via guaranteed bounds

<p>Probabilistic models are an indispensable tool in many scientific fields, from the social and medical sciences to physics and machine learning. In probabilistic programming, such models are specified as computer programs: a flexible yet precise representation that allows for automated analy...

Volledige beschrijving

Bibliografische gegevens
Hoofdauteur:	Zaiser, F
Andere auteurs:	Murawski, A
Formaat:	Thesis
Taal:	English
Gepubliceerd in:	2024
Onderwerpen:	Formal methods (Computer science) Probabilistic Inference Bayesian Statistics Probabilistic Programming Programming languages (Electronic computers)

Gelijkaardige items

Static posterior inference of Bayesian probabilistic programming via polynomial solving
door: Wang, Peixin, et al.
Gepubliceerd in: (2024)

Fast and correct variational inference for probabilistic programming: Differentiability, reparameterisation and smoothing
door: Wagner, D
Gepubliceerd in: (2023)

Semantic Probabilistic Inference of Predictions
door: E. E. Vityaev
Gepubliceerd in: (2017-08-01)

Stan: A Probabilistic Programming Language
door: Bob Carpenter, et al.
Gepubliceerd in: (2017-01-01)

Increasing Interpretability of Bayesian Probabilistic Programming Models Through Interactive Representations
door: Evdoxia Taka, et al.
Gepubliceerd in: (2020-12-01)

GraphPPL.jl: A Probabilistic Programming Language for Graphical Models
door: Wouter W. L. Nuijten, et al.
Gepubliceerd in: (2024-10-01)

Extended Variational Message Passing for Automated Approximate Bayesian Inference
door: Semih Akbayrak, et al.
Gepubliceerd in: (2021-06-01)

Nonparametric Involutive Markov Chain Monte Carlo: a MCMC algorithm for universal probabilistic programming
door: Mak, PYC
Gepubliceerd in: (2022)

Probabilistic inference: Task dependency and individual differences of probability weighting revealed by hierarchical Bayesian modelling
door: Moritz eBoos, et al.
Gepubliceerd in: (2016-05-01)

A History of Probabilistic Inductive Logic Programming
door: Fabrizio eRiguzzi, et al.
Gepubliceerd in: (2014-09-01)

Automating inference, learning, and design using probabilistic programming
door: Rainforth, T
Gepubliceerd in: (2017)

Automating inference for non–standard models
door: Zhou, Y
Gepubliceerd in: (2020)

Probabilistic programming in Python using PyMC3
door: John Salvatier, et al.
Gepubliceerd in: (2016-04-01)

Probabilistic Prediction Model Using Bayesian Inference in Climate Field: A Systematic Literature
door: Evi Ardiyani, et al.
Gepubliceerd in: (2023-07-01)

PyMC: a modern, and comprehensive probabilistic programming framework in Python
door: Oriol Abril-Pla, et al.
Gepubliceerd in: (2023-09-01)

Probabilistic Inference and Dynamic Programming: A Unified Approach to Multi-Agent Autonomous Coordination in Complex and Uncertain Environments
door: Giovanni Di Gennaro, et al.
Gepubliceerd in: (2022-07-01)

Planning as Inference in Epidemiological Dynamics Models
door: Frank Wood, et al.
Gepubliceerd in: (2022-03-01)

Probabilistic Hotspot Prediction Model Based on Bayesian Inference Using Precipitation, Relative Dry Spells, ENSO and IOD
door: Evi Ardiyani, et al.
Gepubliceerd in: (2023-01-01)

Principled Decision-Making Workflow with Hierarchical Bayesian Models of High-Throughput Dose-Response Measurements
door: Eric J. Ma, et al.
Gepubliceerd in: (2021-06-01)

Logic + probabilistic programming + causal laws
door: Vaishak Belle
Gepubliceerd in: (2023-09-01)

Bayesian neural networks using magnetic tunnel junction-based probabilistic in-memory computing
door: Samuel Liu, et al.
Gepubliceerd in: (2022-10-01)

Erratum: Bayesian neural networks using magnetic tunnel junction-based probabilistic in-memory computing
door: Frontiers Production Office
Gepubliceerd in: (2022-12-01)

Bayesian statistic methods and theri application in probabilistic simulation models
door: Sergio Iannazzo
Gepubliceerd in: (2007-03-01)

Probabilistic Logic Models for the Lightning Network
door: Damiano Azzolini, et al.
Gepubliceerd in: (2022-06-01)

Probabilistic Load Forecasting With Reservoir Computing
door: Michele Guerra, et al.
Gepubliceerd in: (2023-01-01)

Enhanced production surveillance using probabilistic dynamic models
door: Ashutosh Tewari, et al.
Gepubliceerd in: (2018-01-01)

Bayesian Modeling for Differential Cryptanalysis of Block Ciphers: A DES Instance
door: Vincenzo Agate, et al.
Gepubliceerd in: (2023-01-01)

Studying Transaction Fees in the Bitcoin Blockchain with Probabilistic Logic Programming
door: Damiano Azzolini, et al.
Gepubliceerd in: (2019-10-01)

An FPGA implementation of Bayesian inference with spiking neural networks
door: Haoran Li, et al.
Gepubliceerd in: (2024-01-01)

Probabilistic Inference with Polymerizing Biochemical Circuits
door: Yarden Katz, et al.
Gepubliceerd in: (2022-04-01)

Sparse Bayesian Broad Learning System for Probabilistic Estimation of Prediction
door: Lili Xu, et al.
Gepubliceerd in: (2020-01-01)

On the Coherence of Probabilistic Relational Formalisms
door: Glauber De Bona, et al.
Gepubliceerd in: (2018-03-01)

Seizure Susceptibility Prediction in Uncontrolled Epilepsy
door: Nhan Duy Truong, et al.
Gepubliceerd in: (2021-09-01)

The predictive view of Bayesian inference
door: Fong, CHE
Gepubliceerd in: (2021)

Design and Implementation of Probabilistic Assignment Management System for a Library Database Using Object Oriented Programming
door: Jamal Hameed
Gepubliceerd in: (2012-12-01)

Survey on Probabilistic Models of Low-Rank Matrix Factorizations
door: Jiarong Shi, et al.
Gepubliceerd in: (2017-08-01)

Probabilistic early warning signals
door: Ville Laitinen, et al.
Gepubliceerd in: (2021-10-01)

TRUCK CRANE HOIST BOOM BUCKLING RELIABILITY ANALYSIS BASED ON PROBABILISTIC AND NON-PROBABILISTIC HYBRID MODEL AND BAYESIAN NETWORK
door: QU FuZheng, et al.
Gepubliceerd in: (2015-01-01)

Bayesian Energy Measurement and Verification Analysis
door: Herman Carstens, et al.
Gepubliceerd in: (2018-02-01)

Using Value-Based Potentials for Making Approximate Inference on Probabilistic Graphical Models
door: Pedro Bonilla-Nadal, et al.
Gepubliceerd in: (2022-07-01)