Bayesian Inference in Generative Models
Explore approximate Bayesian inference methods, including sampling-based techniques and variational inference, with hands-on exercises in implementing algorithms and using probabilistic programming languages.
The Kikuchi Hierarchy and Tensor PCA
Explores advanced algorithms for tensor PCA, introducing a new hierarchy inspired by Kikuchi free energy to improve belief propagation performance and match sum-of-squares methods.
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.
Data Augmentation MCMC for Bayesian Inference from Privatized Data
Explore data augmentation MCMC for Bayesian inference from privatized data, focusing on differential privacy challenges and solutions in statistical analysis.
Optimal Transport for Inverse Problems and Implicit Regularization
Exploring optimal transport theory for inverse problems, addressing nonconvexity and noise sensitivity in least-squares methods, with applications in industry and broader data-fitting challenges.
Bayesian Inference by Program Verification - Joost-Pieter Katoen, RWTH Aachen University
Explore Bayesian inference through program verification, learning how weakest precondition reasoning can be applied to exact inference and analyzing expected sampling times in Bayesian networks.
Variational Bayesian Approximation of Inverse Problems Using Sparse Precision Matrices
Explore Variational Bayes as an efficient alternative to MCMC for solving inverse problems in PDEs, using sparse precision matrices and stochastic optimization for improved computational tractability.
Introduction to Probability and Bayesian Inference - Lecture 1
Explore probability fundamentals and Bayesian inference principles in this comprehensive lecture, laying the groundwork for advanced data assimilation techniques in weather and climate modeling.
Bayesian Inference - Lecture 2
Explore Bayesian inference principles, methods, and applications in data assimilation for weather and climate modeling. Gain insights into uncertainty estimation and practical implementation.
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.
Learning Theory-Informed Priors for Bayesian Inference - A Case Study with Early Dark Energy
Explore novel methods for learning theory-informed priors in Bayesian inference using normalizing flows, with applications to early dark energy models in cosmology.
Data-Driven Closure Modeling Using Derivative-free Kalman Methods
Explore data-driven closure modeling for complex dynamical systems using derivative-free Kalman methods. Learn to improve predictions in turbulence and cloud dynamics with advanced machine learning techniques.
Bayesian Inference of Dependent Population Dynamics in Coalescent Models
Explore Bayesian inference techniques for analyzing dependent population dynamics in coalescent models, focusing on applications in genomics and evolutionary biology.
Variational Gaussian Approximation of the Kushner Optimal Filter
Explore the Variational Gaussian approximation technique for optimizing the Kushner filter, enhancing signal processing and estimation capabilities.
Fast Probabilistic Inference for ODEs with ProbNumDiffEq.jl
Explore probabilistic ODE solvers with ProbNumDiffEq.jl. Learn Bayesian inference for numerical problems, implementation details, and future prospects in the Julia ecosystem.
Bayesian Workflow
Learn Bayesian workflow techniques for probabilistic modeling, including prior specification, model checking, and posterior predictive checks.
Monte Carlo Methods
Explore Monte Carlo methods for probabilistic AI, covering key concepts and applications in this comprehensive tutorial from ProbAI 2024.
SDEs Are All You Need for Latent Variable Models
Explore stochastic differential equations for latent variable models, focusing on applications in probabilistic AI and machine learning.
Bayesian Workflow
Learn Bayesian workflow techniques for probabilistic modeling, from problem formulation to model evaluation and iteration, enhancing your data analysis skills.
Variational Approximations for Bayesian and Semi-Bayesian Inferences
Explore variational approximations in Bayesian inference, focusing on group-variable selection and heteroscedastic Gaussian processes. Learn efficient parameter estimation and process forecasting techniques.