Statistical Inference
Comprehensive exploration of statistical inference, covering likelihood, hypothesis testing, Bayesian statistics, and non-parametric methods for robust data analysis and decision-making.
Statistical Rethinking 2023 - Missing Data
Explore missing data in DAGs and learn Bayesian imputation techniques for handling incomplete datasets in statistical analysis, with practical examples and insights.
Statistical Rethinking 2023 - Multilevel Adventures
Explore multilevel modeling techniques for analyzing clustered data, focusing on varying confounds, district-level effects, and urban-rural differences in Bangladesh fertility studies.
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 2023 - Categories & Curves
Explore statistical concepts like categories, posterior contrasts, and curves in this comprehensive lecture. Learn advanced techniques for data analysis and modeling using Bayesian methods.
Statistical Rethinking 2023 - The Garden of Forking Data
Explore Bayesian statistics through the "Garden of Forking Data" metaphor. Learn about generative models, probability, testing, and posterior distributions for improved statistical reasoning and prediction.
Statistical Rethinking 2022 - Geocentric Models
Explore Bayesian linear regression, covering model language, generative and statistical approaches, validation, and posterior predictions for a comprehensive understanding of statistical modeling.
Statistical Rethinking 2022 - Bayesian Inference
Explore Bayesian inference through practical examples like globe tossing. Learn updating, sampling, and computing distributions using grid approximation and posterior predictive methods.
Science Before Statistics - Causal Inference
Comprehensive workshop on causal inference, covering key concepts like causal design, graph analysis, and Bayesian inference. Equips researchers with tools to navigate complex causal relationships in data analysis.
Bayesian Statistics Without Frequentist Language
Explore Bayesian statistics from a fresh perspective, focusing on its core concepts and applications without relying on traditional frequentist terminology.
Scaling Up Bayesian Inference for Big and Complex Data
Explore advanced Bayesian inference techniques for large-scale datasets, focusing on computational challenges and innovative solutions in machine learning and deep learning applications.
Perceptual Organization From a Bayesian Point of View
Explore Bayesian approach to perceptual organization, examining how the visual system groups images into distinct units through inverse inference and mixture estimation techniques.
Development of Intelligence - Josh Tenenbaum: Bayesian Inference
Explore Bayesian inference and cognitive neuroscience with Josh Tenenbaum, delving into AI challenges, scene understanding, and advanced mathematical concepts for intelligence development.
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.
Mathematical and Statistical Modelling in the Fight to Control Tuberculosis by Emma McBryde
Exploring mathematical models to combat tuberculosis, from basic concepts to advanced simulations. Insights on disease dynamics, latency, mortality, and regional applications in the Asia Pacific.
A Brief Introduction to Nested Sampling - IPAM at UCLA
Explore Nested Sampling for Bayesian model uncertainty and selection. Learn its principles, advantages, and recent extensions like Dynamic Nested Sampling for estimating marginal likelihoods and posterior distributions.
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.
Statistics and Data Analysis for Cosmology - Lecture 1
Explore probability concepts in cosmology, including joint and conditional probability, PDFs, independence, Bayes' theorem, and data modeling for analyzing cosmic phenomena.
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.