In statistics, an adaptive estimator is an estimator in a parametric or semiparametric model with nuisance parameters such that the presence of these nuisance parameters does not affect efficiency of estimation. (Wikipedia).

(ML 11.1) Estimators

Definition of an estimator. Examples of estimators. Definition of an unbiased estimator.

From playlist Machine Learning

Raúl Tempone: Adaptive strategies for Multilevel Monte Carlo

Abstract: We will first recall, for a general audience, the use of Monte Carlo and Multi-level Monte Carlo methods in the context of Uncertainty Quantification. Then we will discuss the recently developed Adaptive Multilevel Monte Carlo (MLMC) Methods for (i) It Stochastic Differential Equ

From playlist Probability and Statistics

Maximum Likelihood Estimation Examples

http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Three examples of applying the maximum likelihood criterion to find an estimator: 1) Mean and variance of an iid Gaussian, 2) Linear signal model in

From playlist Estimation and Detection Theory

Introduction to Estimation Theory

http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. General notion of estimating a parameter and measures of estimation quality including bias, variance, and mean-squared error.

From playlist Estimation and Detection Theory

A Google TechTalk, presented by Adam Smith, 2021/11/9 ABSTRACT: Differentially Private Covariance-Adaptive Mean Estimation Covariance-adaptive mean estimation is a fundamental problem in statistics, where we are given n i.i.d. samples from a d-dimensional distribution with mean $\mu$ and

What is adaptive quadrature? Join me on Coursera: https://www.coursera.org/learn/numerical-methods-engineers Lecture notes at http://www.math.ust.hk/~machas/numerical-methods-for-engineers.pdf Subscribe to my channel: http://www.youtube.com/user/jchasnov?sub_confirmation=1

From playlist Numerical Methods for Engineers

DDPS | Towards reliable, efficient, and automated model reduction of parametrized nonlinear PDEs

Description: Many engineering tasks, such as parametric study and uncertainty quantification, require rapid and reliable solution of partial differential equations (PDEs) for many different configurations. In this talk, we consider goal-oriented model reduction of parametrized nonlinear PD

CIRM VIRTUAL EVENT Recorded during the meeting "Mathematical Methods of Modern Statistics 2" the June 09, 2020 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians

From playlist Virtual Conference

Rigorous Data Dredging...Data Analysis - Aaron Roth

Differential Privacy Symposium: Four Facets of Differential Privacy Saturday, November 12, 2016 https://www.ias.edu/differential-privacy More videos on http://video.ias.edu

From playlist Differential Privacy Symposium - November 12, 2016

Martin Vohralík: Adaptive inexact Newton methods and their application to multi-phase flows

Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b

From playlist Numerical Analysis and Scientific Computing

Linear regression (5): Bias and variance

Inductive bias; variance; relationship to over- & under-fitting

From playlist cs273a

Experimental optical phase measurement at the exact (...) - H. Wiseman - PRACQSYS 2018 - CEB T2 2018

Howard M. Wiseman (Centre for Quantum Computation and Communication Technology - Australian Research Council & Centre for Quantum Dynamics, Griffith University, Brisbane, Queensland, Australia) / 03.07.2018 Experimental optical phase measurement at the exact Heisenberg limit The task of

Engineering CEE 20: Engineering Problem Solving. Lecture 24

UCI CIvil & Environmental Engineering 20 Engineering Problem Solving (Spring 2013) Lec 24. Engineering Problem Solving View the complete course: http://ocw.uci.edu/courses/cee_20_introduction_to_computational_engineering_problem_solving.html Instructor: Jasper Alexander Vrugt, Ph.D. Licen

From playlist Engineering CEE 20: Engineering Problem Solving

Elisabeth Gassiat: Bayesian multiple testting for dependent data and hidden Markov... - lecture 2

HYBRID EVENT Recorded during the meeting "End-to-end Bayesian Learning Methods " the October 28, 2021 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians on CIRM's

From playlist Probability and Statistics

On Adaptation, Epistasis and Fitness Landscapes - Lecture 1 by Claudia Bank

ORGANIZERS : Vidyanand Nanjundiah and Olivier Rivoire DATE & TIME : 16 April 2018 to 26 April 2018 VENUE : Ramanujan Lecture Hall, ICTS Bangalore This program is aimed at Master's- and PhD-level students who wish to be exposed to interesting problems in biology that lie at the biology-

From playlist Living Matter 2018

Adaptive Sampling via Sequential Decision Making - András György

The workshop aims at bringing together researchers working on the theoretical foundations of learning, with an emphasis on methods at the intersection of statistics, probability and optimization. Lecture blurb Sampling algorithms are widely used in machine learning, and their success of

Michael Bertolacci - AdaptSPEC-X: Spectral analysis of multiple non stationary time series

Dr Michael Bertolacci (University of Wollongong) presents “AdaptSPEC-X: Spectral analysis of multiple non stationary time series”, 08/10/2020. Seminar organised by ANU.

From playlist Statistics Across Campuses

## Related pages

Semiparametric model | Nuisance parameter | Statistics | Score (statistics) | Normal distribution | Estimator | Parametric model