Spectral Methods Meets Asymmetry: Two Recent Stories - Lecture 4
Explore spectral methods for asymmetric data matrices, covering top-K ranking and matrix de-noising. Learn advantages over traditional approaches in data analysis and estimation.
Integrating Mechanistic Models with Computational Statistics and Machine Learning - New Insights in Biology
Explore integrating mechanistic models with computational statistics and machine learning for new insights in cell and developmental biology. Tackle challenges in analyzing complex quantitative data.
Skew-Symmetric Approximations of Posterior Distributions - Colloque des Sciences Mathématiques du Québec
Explore unified skew-normal distributions as conjugate priors for discretized Gaussian linear regressions, enhancing Bayesian inference and computation in regression models across various fields.
Adaptive Bayesian Predictive Inference
Explore adaptive Bayesian predictive inference for high-dimensional sparse mean estimation, examining sparsity priors and their performance in predictive density estimation under Kullback-Leibler loss.
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.
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.
Regressing Multivariate Gaussian Distribution on Vector Covariates for Co-expression Network Analysis
Explore Fréchet regression for analyzing gene co-expression networks using population-level single-cell data. Learn to test associations between covariance matrices and covariates.
Interlacing Families and Ramanujan Graphs - #ICBS2024
Explore Ramanujan Graphs, interlacing families, and their connections to various mathematical fields. Gain insights into recent developments and open problems in graph theory.
Causal Inference with Mendelian Randomization in Biomedical Studies
Explore Mendelian Randomization for causal inference in biomedical research. Learn innovative methods, overcome challenges, and gain insights into complex disease relationships through simulation and case studies.
The Chromatic Number of Random Graphs
Explore the chromatic number of random graphs, focusing on lower bounds for G(n,1/2) and its limiting distribution. Discover recent advancements in graph theory and probabilistic methods.
Recent Progress in Random Matrix Theory
Explore recent advancements in random matrix theory, focusing on spectral statistics of normalized adjacency matrices in random d-regular graphs and their eigenvalue rigidity.
The KPZ Fixed Point - Understanding Random Growing Interfaces
Explore the KPZ universality class, its fixed point, and connections to random matrix theory and integrable systems in growing interfaces and exclusion processes.
Stationary Random Subgroups and Injectivity Radius in Negative Curvature
Explore stationary random subgroups and injectivity radius in negative curvature, focusing on rank one simple Lie groups and hyperbolic manifolds. Gain insights into recent theorems and their applications.
Canonical Colourings in Random Graphs
Explore canonical colourings in random graphs with Nina Kamčev, delving into extremal combinatorics, Ramsey theory, and asymptotic enumeration.
Resolution of the Quadratic Littlewood-Offord Problem
Explore the resolution of the Quadratic Littlewood-Offord problem with expert Mathew Kwan, delving into combinatorics and its probabilistic connections.
Chinese Restaurant Process and Statistical Inference
Explore Chinese Restaurant process and statistical inference with Prof. Kiranmoy Das, covering Bayesian modeling and longitudinal data analysis applications.
Highlights from the History of Probability
Explore key milestones in probability theory, from Hilbert's problems to martingales, with insights on Brownian motion, Central Limit Theorem, and stochastic processes.
Learning Causal Models via Algebraic Constraints
Explorez les modèles causaux linéaires et leurs graphes dirigés. Découvrez des méthodes algébriques pour identifier les relations causales à partir de données observationnelles, même avec des cycles ou variables cachées.
Combinatorial Testing Problems - Lecture 1
Explore combinatorial testing problems, including hidden clique and high-dimensional random geometric graphs, with insights on statistical challenges and estimation techniques.
Mean Estimation: Median of Means Tournaments - Lecture 2
Explore mean estimation techniques using median of means tournaments in this advanced statistical lecture by Gabor Lugosi from Pompeu Fabra University.
Pareto, Power Laws, and Fat Tails: A Beginner's Guide to Non-Gaussian Statistics
Explore power laws, fat tails, and their impact on statistical analysis. Learn why traditional methods fall short and gain insights into analyzing extreme events in various domains.