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.
Statistical Models: Linking Data to Theory - Class 4
Explore statistical models linking ecological data to theory, focusing on advanced techniques for quantitative analysis in ecology.
Better Priors for Everyone - Keynote
Explore advanced techniques for improving prior distributions in probabilistic models, enhancing Bayesian inference across various applications.
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.
Building a Measurement System for Personalization - A Bayesian Approach
Explore Bayesian approaches for measuring personalization effectiveness, optimizing customer assignments, and designing MLOps-inspired measurement platforms for complex hierarchical experiments.
Bayesian Markov Switching Space-Time Model for Zika in Colombia
Explorez un modèle bayésien spatio-temporel à changement de régime markovien pour l'épidémie de Zika en Colombie, examinant les facteurs d'émergence, de transmission et de persistance de la maladie.
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.
Bayesian Quantile Joint Modeling of Multivariate Longitudinal and Time-to-Event Data
Explore Bayesian quantile joint modeling for multivariate longitudinal and time-to-event data, focusing on non-Gaussian outcomes and time-varying covariates in medical research applications.
Automated Scalable Bayesian Inference via Data Summarization - 2018
Explore scalable Bayesian inference techniques using data summarization. Learn about coresets, theoretical guarantees, and practical applications in large-scale data settings.
A Bayesian View of Inductive Learning in Humans and Machines - 2004
Explore how humans make successful generalizations from limited evidence through Bayesian inferences constrained by implicit domain theories, with applications to machine learning.
The Signal and the Noise by Nate Silver - Part 2
Explore Nate Silver's insights on data analysis and prediction in "The Signal and the Noise" through an expert review of key concepts and applications.
Empirical Bayes and Its Applications: Shrinkage, Hypothesis Testing, and More
Explore Empirical Bayes methods for shrinkage, hypothesis testing, and genomic studies. Learn statistical techniques to estimate and test effects in multiple conditions.
Bayesian Inference of Causal Graphs: Current Status and Future Directions
Explore Bayesian inference for causal discovery, examining recent progress, challenges, and assumptions in modeling cause-effect relationships from observational data.
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.