Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics
Explore accelerated molecular dynamics simulation using Timewarp, a transferable AI method for efficient sampling of molecular systems and discovery of metastable states.
Bayesian Inference Using Probabilistic Programming
Explore Bayesian inference techniques and probabilistic programming concepts with expert insights from Hong Ge at LAFI'24.
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.
Cognitive Computational Modeling Made Easy with Julia
Explore cognitive computational modeling using Julia, featuring ActionModels.jl for flexible simulations and HierarchicalGaussianFiltering.jl for advanced learning and perception models.
Statistical Analysis in Cosmology: Goodness-of-Fit and Model Selection - Part 2
Explore advanced statistical methods in cosmology, including model selection, MCMC, chi-square, and p-value analysis for robust data interpretation.
Probabilistic Programming Tutorial - Part 2
Explore advanced concepts in probabilistic programming, focusing on practical applications and techniques for scientific computing and data analysis.
Better Science Through Better Bayesian Computation - 2015
Explore Bayesian computation for data analysis in science and engineering. Learn about Bayesian optimization and scalable Markov chain Monte Carlo techniques for improved hypothesis testing and decision-making.
Probabilistic Programming Tutorial - Part 1
Explore probabilistic programming fundamentals and applications with MIT expert Vikash Mansinghka in this comprehensive tutorial.
Statistical Analysis in Cosmology - Part 1
Explore Bayesian statistics, likelihood sampling, chi-square, and covariance matrix in cosmology. Learn parameter estimations in 2-point statistics and Markov Chain Monte Carlo.
Wasserstein Gradient Flows and Applications to Sampling in Machine Learning - Lecture 1
Explore Wasserstein gradient flows and their applications to sampling in machine learning through an in-depth lecture by Anna Korba.
Action Matching: Learning Stochastic Dynamics from Samples
Learn stochastic dynamics from samples using action matching techniques. Explore innovative approaches to understanding and modeling complex systems through data-driven methods.
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.