Workshop on Parameter Estimation with Bilby by Greg Ashton
Learn parameter estimation techniques for gravitational wave analysis using bilby software, focusing on Bayesian inference and practical applications in astrophysics.
Time Series Modelling and State Space Models - Professor Chris Williams, University of Edinburgh
Explore time series modeling, including ARMA models, Hidden Markov Models, and Kalman filtering. Learn parameter estimation, inference techniques, and advanced topics in state-space models.
Recent Progress in High-Dimensional Learning
Explore recent advancements in high-dimensional learning, covering robust estimation, maximum likelihood, and algorithmic approaches for handling noise and adversarial scenarios.
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.
Farzana Nasrin - Random Persistence Diagram Generator
Explore a method for generating random persistence diagrams using parametric models and Markov chain Monte Carlo algorithms, with applications in material science and topology.
Workshop on Parameter Estimation with Bilby by Greg Ashton
Learn advanced techniques for gravitational wave parameter estimation using bilby software, focusing on practical applications and data analysis methods.
Small Covers for Near-zero Sets of Polynomials and Learning Latent Variable Models
Explore techniques for parameter estimation in Gaussian mixture models and mixtures of linear regressions, focusing on dimension reduction and higher moments to overcome obstacles in learning latent variable models.
Point Estimation Combining Measurements and Fitting - Lecture 2
Explore advanced statistical techniques for point estimation, measurement combination, and data fitting in high-energy physics, enhancing analysis skills for complex experimental data.
Data to Differential Equations: Discovering Mathematical Models for Biological Systems
Explore data-driven techniques for discovering governing equations in complex biological systems, focusing on PDEs and innovative mathematical methods for modeling phenomena like arterial flow and tissue fracture.
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.
Parameter Estimation and Interpretability in Bayesian Mixture Models
Explore posterior contraction in Bayesian mixture modeling, examining parameter estimation and model interpretability under correct and misspecified conditions. Gain insights into tradeoffs between expressiveness and interpretability.
Limits of Noisy Quantum Metrology with Restricted Quantum Controls
Delve into quantum metrological limits, exploring how restricted quantum controls affect parameter estimation precision and the achievability of Heisenberg and standard quantum limits in noisy systems.
Black Hole Astrophysics With Gravitational-Wave Catalogs - IPAM at UCLA
Explore black hole astrophysics through gravitational-wave data, uncovering insights on mass distribution, spin, and redshift evolution. Learn about pair-instability mass gap and formation-to-merger time delays.
Cosmology with Dark Sirens - Attempting to Solve the Hubble Constant Tension
Explore gravitational waves for cosmology, focusing on dark sirens and galaxy catalogs to address Hubble constant tension. Learn challenges in GW-based cosmological parameter estimation.
Power Spectral Density Uncertainty and Gravitational-Wave Parameter Estimation
Explore power spectral density uncertainty in gravitational-wave parameter estimation, its impact on Bayesian analysis, and methods to incorporate this uncertainty for more accurate results in compact binary coalescence detection.
Waveform Systematics in the Gravitational-Wave Inference of Tidal Parameters
Exploración de sistemáticas en formas de onda para inferir parámetros de marea y ecuación de estado en señales de ondas gravitacionales de sistemas binarios de estrellas de neutrones.
Gravitational Wave Data Analysis - Part 2
Explore advanced techniques in gravitational wave data analysis, focusing on signal processing and statistical methods for detecting and characterizing cosmic events.
Gravitational Wave Data Analysis - Part 1
Explore gravitational wave data analysis techniques, focusing on fundamental concepts and methodologies used in detecting and interpreting cosmic signals.
Statistical Methods for Data Analysis in Particle Physics - Lecture 3
Explore advanced statistical techniques for analyzing particle physics data, focusing on hypothesis testing, confidence intervals, and likelihood methods in experimental research.
Bayesian Learning: Maximum A Posteriori and Maximum Likelihood - Lecture 24
Delve into Bayesian learning principles, exploring maximum a posteriori and maximum likelihood learning criteria through practical examples and comprehensive demonstrations.
Bayesian Learning and Maximum Likelihood Methods - Lecture 23
Explore probabilistic learning criteria through maximum a posteriori and maximum likelihood approaches, with practical examples of Bayesian learning applications.