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Probabilistic Programming Courses and Certifications

Master Bayesian inference, uncertainty quantification, and statistical modeling using PyMC3, Pyro, and other probabilistic frameworks. Build expertise through YouTube tutorials from leading conferences and institutions, applying probabilistic methods to genomics, decision theory, and advanced computational problems.

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Probabilistic Programming Courses and Certifications

Scaling Decision-Theoretic Probabilistic Programming Through Factorization

Semantics for Reactive Probabilistic Programming

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

Using the Pyro Probabilistic Programming Language for Statistical Genomics

Multi-Language Probabilistic Programming

A Domain-Specific Probabilistic Programming Language for Reasoning about Reasoning - Or: A Memo on memo

Turning Probabilistic Reasoning into Programming

Steps Towards More Human-Like Learning in Machines - Josh Tenenbaum

Introduction to Machine Learning - Professor Zoubin Ghahramani, Cambridge University

Probabilistic Numerics for Inference with Simulations

Semantic Models for Higher-Order Bayesian Inference - Sam Staton, University of Oxford

Learning and Amortized Inference in Probabilistic Programs

Lexicographic Ranking Supermartingales: An Efficient Approach to Termination of Probabilistic Programs

Proving Expected Sensitivity of Probabilistic Programs

Bluebell: An Alliance of Relational Lifting and Independence For Probabilistic Reasoning

Probabilistic Programming Courses and Certifications

Scaling Decision-Theoretic Probabilistic Programming Through Factorization

Semantics for Reactive Probabilistic Programming

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

Using the Pyro Probabilistic Programming Language for Statistical Genomics

Multi-Language Probabilistic Programming

A Domain-Specific Probabilistic Programming Language for Reasoning about Reasoning - Or: A Memo on memo

Turning Probabilistic Reasoning into Programming

Steps Towards More Human-Like Learning in Machines - Josh Tenenbaum

Introduction to Machine Learning - Professor Zoubin Ghahramani, Cambridge University

Probabilistic Numerics for Inference with Simulations

Semantic Models for Higher-Order Bayesian Inference - Sam Staton, University of Oxford

Learning and Amortized Inference in Probabilistic Programs

Lexicographic Ranking Supermartingales: An Efficient Approach to Termination of Probabilistic Programs

Proving Expected Sensitivity of Probabilistic Programs

Bluebell: An Alliance of Relational Lifting and Independence For Probabilistic Reasoning

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