When Do Dependencies in Your Data Help? - Emerging Generalization Settings
Explore how dependencies in data can overcome computational barriers in learning graphical models, using the Glauber dynamics process as a natural data generation method.
Using Classical Algorithms for Modern Applications: MCMC for Tail Calculations, EM - Lecture
Explore classical algorithms applied to modern challenges: MCMC for tail probability estimation and EM for PET reconstruction. Gain insights into innovative adaptations of established methods for cutting-edge problems.
Bayesian Statistics - A Comprehensive Course
Comprehensive introduction to Bayesian statistics, covering key concepts, probability distributions, and practical applications with minimal mathematical complexity.
Statistical Rethinking 2023 - Markov Chain Monte Carlo
Explore Markov Chain Monte Carlo methods, from basic concepts to advanced techniques like Hamiltonian Monte Carlo, with practical applications and diagnostics for statistical modeling.
Statistical Rethinking - Markov Chain Monte Carlo
Explore Markov chain Monte Carlo and Hamiltonian Monte Carlo methods, including practical implementation in Stan and diagnostic techniques for statistical modeling.
Sampling Crash Course
Explore geometric methods in optimization and sampling, covering direct methods, Markov chain Monte Carlo, and probabilistic approaches for efficient algorithm design and analysis.
Enhancing Markov Chain Monte Carlo Sampling Methods with Deep Learning
Explore advanced techniques combining Markov Chain Monte Carlo sampling with deep learning, focusing on importance sampling, normalizing flows, and non-equilibrium methods for enhanced efficiency in high-dimensional problems.
MCMC Training of Bayesian Neural Networks
Explore Bayesian Neural Networks, MCMC methods, and their applications in machine learning with Prof. Radford Neal. Gain insights into flexible modeling and practical implementations.
Stratification for Markov Chain Monte Carlo
Explore advanced Markov Chain Monte Carlo techniques, focusing on stratification methods for complex systems modeling and statistical inference in various scientific domains.
Statistical Rethinking 2023
Comprehensive exploration of advanced statistical concepts, from basic models to complex techniques like Gaussian Processes and Generalized Linear Models, enhancing analytical skills for data interpretation.
From Quantum Entanglement to Future Data-Driven Engineering - Rothschild Lecture
Explore high-dimensional Bayesian statistics in engineering, focusing on efficient data-physical model integration and uncertainty quantification. Learn about multilevel MCMC and measure-transport approaches.
Multiscale Generalized Hamiltonian Monte Carlo with Delayed Rejection
Explore advanced sampling techniques combining generalized Hamiltonian Monte Carlo and delayed rejection for efficient multiscale distribution sampling, overcoming limitations of standard HMC methods.
Markov Random Fields, Markov Chains, and Markov Logic Networks - Probabilistic Graphical Models Tutorial
Explore Markov Random Fields, Logic Networks, and Chains. Dive into probabilistic graphical models with Professor Simari, covering key concepts and applications in AI and machine learning.
Monte Carlo Methods
Explore Monte Carlo methods for probabilistic AI, covering key concepts and applications in this comprehensive tutorial from ProbAI 2024.
Bayesian Workflow
Learn Bayesian workflow techniques for probabilistic modeling, from problem formulation to model evaluation and iteration, enhancing your data analysis skills.
Sampling through Exploration Exploitation in Markov Chain Monte Carlo
Explore an innovative Markov chain Monte Carlo algorithm combining global proposals and mobile moves for efficient, parallelizable sampling in various applications.
ME3255 - Quantitative Descriptions on Monte Carlo Models
Explore Monte Carlo models through quantitative descriptions, enhancing understanding of statistical simulation techniques and their applications in engineering and science.
Mathematical Foundation of Various MCMC Methods
Explore advanced MCMC methods for combinatorial optimization, including multi-spin flip techniques. Compare mathematical and numerical results to evaluate efficiency in different contexts.
An Exploration-Agnostic Characterization of the Ergodicity of Parallel Tempering
Explore non-reversible parallel tempering's ergodicity in complex distributions, examining its efficiency and comparing it to classical methods through theoretical analysis and simulations.
Functional Priors for Bayesian Deep Learning
Explore functional priors for Bayesian deep learning, focusing on imposing Gaussian process priors on neural networks through Wasserstein distance minimization.
Timewarp: Transferable Acceleration of Molecular Dynamics by Time-Coarsened Dynamics
Explore Timewarp: a transferable method for accelerating molecular dynamics simulations using normalizing flows, enhancing sampling efficiency for equilibrium properties of molecular systems.