Probabilistic Programming Courses and Certifications

ACM SIGPLAN

Bayesian Inference Using Probabilistic Programming

Explore Bayesian inference techniques and probabilistic programming concepts with expert insights from Hong Ge at LAFI'24.

• YouTube
• 1 hour 2 minutes
• On-Demand
• Free Video

ACM SIGPLAN

Foundations of Type-Driven Probabilistic Modelling

Explore type-driven abstractions for probabilistic modelling, covering discrete probability and quasi-Borel spaces. Gain hands-on experience through accompanying exercises for self-study.

• YouTube
• 1 hour 55 minutes
• On-Demand
• Free Video

ACM SIGPLAN

Bit Blasting Probabilistic Programs

Explore a novel approach to hybrid probabilistic programs using bit blasting for efficient discretization and inference. Learn about HyBit system and its benefits over existing methods.

• YouTube
• 20 minutes
• On-Demand
• Free Video

ACM SIGPLAN

Compiling Probabilistic Programs for Variable Elimination with Information Flow

Explore a novel approach to variable elimination and marginal inference in probabilistic programming, featuring bounded recursion and discrete distributions, with compiler-based optimization techniques.

• YouTube
• 19 minutes
• On-Demand
• Free Video

ACM SIGPLAN

Equivalence and Similarity Refutation for Probabilistic Programs

Explore a novel method for refuting equivalence and similarity in probabilistic programs, offering automated analysis with formal guarantees for infinite-state systems.

• YouTube
• 18 minutes
• On-Demand
• Free Video

ACM SIGPLAN

Scaling Decision-Theoretic Probabilistic Programming Through Factorization

Explore dappl, a new probabilistic programming language for decision-making, offering scalable MEU reasoning and expressive modeling of complex real-world tasks.

• YouTube
• 18 minutes
• On-Demand
• Free Video

ACM SIGPLAN

Automated Expected Value Analysis of Recursive Programs

Explore automated inference of expected result values in probabilistic programs with recursion, procedures, and local variables. Learn about term representation and first-order constraints for analysis automation.

• YouTube
• 17 minutes
• On-Demand
• Free Video

ACM SIGPLAN

Formally Verified Samplers from Probabilistic Programs with Loops and Conditioning

Explore Zar: a verified compiler for discrete probabilistic programs, transforming cpGCL to executable samplers. Learn about choice-fix trees, interaction trees, and sampling equidistribution theorem.

• YouTube
• 20 minutes
• On-Demand
• Free Video

ACM SIGPLAN

Lilac: A Modal Separation Logic for Conditional Probability

Explore Lilac, a novel separation logic for probabilistic programs, introducing a modality for conditional probability reasoning and validating its effectiveness through formal verification examples.

• YouTube
• 19 minutes
• On-Demand
• Free Video

ACM SIGPLAN

Probabilistic Programming Interfaces for Random Graphs: Markov Categories, Graphons, and Nominal Sets - POPL 2024

Explore semantic models of probabilistic programming languages over graphs, connecting graphons to equational theories through Markov categories, measure-theoretic probability, and nominal sets.

• YouTube
• 19 minutes
• On-Demand
• Free Video

ACM SIGPLAN

Programmatic Strategy Synthesis: Resolving Nondeterminism in Probabilistic Programs

Explore programmatic strategy synthesis for resolving nondeterminism in probabilistic programs using deductive verification techniques and weakest precondition-style frameworks.

• YouTube
• 17 minutes
• On-Demand
• Free Video

ACM SIGPLAN

Guaranteed Bounds for Discrete Probabilistic Programs with Loops via Generating Functions - LAFI'24

Explore a technique for finding guaranteed bounds on posterior distributions in probabilistic programs with loops using probability generating functions and inductive invariants.

• YouTube
• 10 minutes
• On-Demand
• Free Video

ACM SIGPLAN

An Intrinsically-typed Probabilistic Programming Language in Coq - Extended Abstract

Explore formalization of intrinsically-typed probabilistic programming language in Coq, focusing on syntax, evaluation, and formal verification applications.

• YouTube
• 24 minutes
• On-Demand
• Free Video

Conf42

Probabilistic Programming in Python - Conf42 Python 2024

Explore Bayesian statistics, probabilistic modeling, and MCMC in Python. Learn key concepts and practical applications through demonstrations and workflow explanations.

• YouTube
• 14 minutes
• On-Demand
• Free Video

Bayesian Networks 1 - Inference - Stanford CS221: AI

Explore Bayesian networks for AI, covering probabilistic inference, modeling, and applications in object tracking, language modeling, and document classification.

• YouTube
• 1 hour 22 minutes
• On-Demand
• Free Online Course