Decision Science with Probabilistic Programming
Explore generative models and probabilistic programming for decision-making under uncertainty, integrating with robust optimization techniques to enhance business strategies.
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.
Effect Handlers for Programmable Inference - Haskell 2023
Explore effect handlers for modular inference programming in probabilistic algorithms, revealing high-level structures and enabling easy customization of Metropolis-Hastings and particle filtering methods.
Probabilistic Programming with Stochastic Probabilities
Explore a novel approach to probabilistic programming languages using stochastic probability estimation, enabling unrestricted stochastic control flow and programmable inference with custom proposals.
Probabilistic Circuits - Lecture on Advanced AI Techniques
Explore probabilistic circuits with YooJung Choi, delving into advanced AI concepts at the intersection of symbolic methods and deep learning.
What's New with Turing.jl and Its Ecosystem
Explore updates to Turing.jl, a probabilistic programming framework in Julia for Bayesian inference, and its expanding ecosystem of modular packages for enhanced usability and extensibility.
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.
Probabilistic Programming Tutorial - Part 2
Explore advanced concepts in probabilistic programming, focusing on practical applications and techniques for scientific computing and data analysis.
Probabilistic Programming Tutorial - Part 1
Explore probabilistic programming fundamentals and applications with MIT expert Vikash Mansinghka in this comprehensive tutorial.
Introduction to Model Fitting - Gravitational Lensing and Graphical Modeling
Explore gravitational lensing model fitting techniques, from traditional methods to innovative graphical modeling, and learn about PyAutoFit for streamlined probabilistic programming.
Learning and Amortized Inference in Probabilistic Programs
Explore probabilistic programming, amortized inference, and novel learning methods like reweighted wake-sleep and thermodynamic variational objective for efficient Bayesian inference in complex models.
Probabilistic Modeling and Programming Tutorial - Lecture 1
Explore probabilistic modeling and programming techniques with expert guidance, enhancing your AI and machine learning skills through hands-on tutorials and practical examples.
DLSS 5 - Нейрослоп или революция? | Стрим, общение
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