Evolutionary computation | Stochastic optimization
Estimation of distribution algorithms (EDAs), sometimes called probabilistic model-building genetic algorithms (PMBGAs), are stochastic optimization methods that guide the search for the optimum by building and sampling explicit probabilistic models of promising candidate solutions. Optimization is viewed as a series of incremental updates of a probabilistic model, starting with the model encoding an uninformative prior over admissible solutions and ending with the model that generates only the global optima. EDAs belong to the class of evolutionary algorithms. The main difference between EDAs and most conventional evolutionary algorithms is that evolutionary algorithms generate new candidate solutions using an implicit distribution defined by one or more variation operators, whereas EDAs use an explicit probability distribution encoded by a Bayesian network, a multivariate normal distribution, or another model class. Similarly as other evolutionary algorithms, EDAs can be used to solve optimization problems defined over a number of representations from vectors to LISP style S expressions, and the quality of candidate solutions is often evaluated using one or more objective functions. The general procedure of an EDA is outlined in the following: t := 0initialize model M(0) to represent uniform distribution over admissible solutionswhile (termination criteria not met) do P := generate N>0 candidate solutions by sampling M(t) F := evaluate all candidate solutions in P M(t + 1) := adjust_model(P, F, M(t)) t := t + 1 Using explicit probabilistic models in optimization allowed EDAs to feasibly solve optimization problems that were notoriously difficult for most conventional evolutionary algorithms and traditional optimization techniques, such as problems with high levels of epistasis. Nonetheless, the advantage of EDAs is also that these algorithms provide an optimization practitioner with a series of probabilistic models that reveal a lot of information about the problem being solved. This information can in turn be used to design problem-specific neighborhood operators for local search, to bias future runs of EDAs on a similar problem, or to create an efficient computational model of the problem. For example, if the population is represented by bit strings of length 4, the EDA can represent the population of promising solution using a single vector of four probabilities (p1, p2, p3, p4) where each component of p defines the probability of that position being a 1. Using this probability vector it is possible to create an arbitrary number of candidate solutions. (Wikipedia).
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
What is a Sampling Distribution?
Intro to sampling distributions. What is a sampling distribution? What is the mean of the sampling distribution of the mean? Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with more than 20 different techniques: https://prof-essa.creat
From playlist Probability Distributions
Sampling Distribution of the PROPORTION: Friends of P (12-2)
The sampling distribution of the proportion is the probability distribution of all possible values of the sample proportions. It is analogous to the Distribution of Sample Means. When the sample size is large enough, the sampling distribution of the proportion can be approximated by a norm
From playlist Sampling Distributions in Statistics (WK 12 - QBA 237)
Calculate Sample Size Interval of A Population Mean
How to calculate the sample size. Includes discussion and visualization of how sample sizes changes when standard deviation, margin of error changes too. Calculating Sample Size of A Proportion https://youtu.be/ni3YAUF7qy4 Derving Equation https://youtu.be/5LvL1kbNoCM Calculating z sco
From playlist Sample Size
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
Finding The Confidence Interval of a Population Proportion Using The Normal Distribution
This statistics video tutorial explains how to find the confidence interval of a population proportion using the normal distribution. It also explains how to calculate the margin of error also known as the error bound for the true proportion. it discusses how to calculate the sample size
From playlist Statistics
Excel 2013 Statistical Analysis #50: t Distribution Confidence Intervals Sigma NOT Known 3 Examples
Download files (which file shown at begin of video): https://people.highline.edu/mgirvin/AllClasses/210Excel2013/Ch08/Ch08.htm Topics in this video: 1. (00:12) What is the t Distribution and when do we have to use it? Create Confidence Intervals when Population Standard Deviation is not kn
From playlist Excel for Statistical Analysis in Business & Economics Free Course at YouTube (75 Videos)
Excel 2013 Statistical Analysis #31: Create Discrete Probability Distribution, Calculate Mean and SD
Download files (which file shown at begin of video): https://people.highline.edu/mgirvin/AllClasses/210Excel2013/Ch05/Ch05.htm Topics in this video: 1. (00:12) Discussion about Discrete Probability Distributions, Random Variables, Continuous Random Variables, Discrete Random Variables, Dis
From playlist Excel for Statistical Analysis in Business & Economics Free Course at YouTube (75 Videos)
Sampling Distributions of Means
This is an old video. See StatsMrR.com for access to hundreds of 1-3 minute, well-produced videos for learning Statistics. In this older video: Understanding and working with sampling distributions of means. Calculating the mean and standard deviation and the probability associated with
From playlist Older Statistics Videos and Other Math Videos
Estimating the mean of a real valued distribution - Paul Valiant
Members’ Seminar Topic: Estimating the mean of a real valued distribution Speaker: Paul Valiant Affiliation: Brown University; von Neumann Fellow, School of Mathematics Date: March 15, 2021 For more video please visit http://video.ias.edu
From playlist Mathematics
EM Algorithm In Machine Learning | Expectation-Maximization | Machine Learning Tutorial | Edureka
** Machine Learning Certification Training: https://www.edureka.co/machine-learning-certification-training ** This Edureka video on 'EM Algorithm In Machine Learning' covers the EM algorithm along with the problem of latent variables in maximum likelihood and Gaussian mixture model. Follo
From playlist Machine Learning Algorithms in Python (With Demo) | Edureka
Learning Minimax Estimators Via Online Learning by Praneeth Netrapalli
PROGRAM: ADVANCES IN APPLIED PROBABILITY ORGANIZERS: Vivek Borkar, Sandeep Juneja, Kavita Ramanan, Devavrat Shah, and Piyush Srivastava DATE & TIME: 05 August 2019 to 17 August 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore Applied probability has seen a revolutionary growth in resear
From playlist Advances in Applied Probability 2019
Environment oblivious risk-aware bandit algorithms by Jayakrishnan Nair
PROGRAM: ADVANCES IN APPLIED PROBABILITY ORGANIZERS: Vivek Borkar, Sandeep Juneja, Kavita Ramanan, Devavrat Shah, and Piyush Srivastava DATE & TIME: 05 August 2019 to 17 August 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore Applied probability has seen a revolutionary growth in resear
From playlist Advances in Applied Probability 2019
Outlier-Robust Estimation via Sum-of-Squares - Pravesh Kothari
Computer Science/Discrete Mathematics Seminar II Topic: Outlier-Robust Estimation via Sum-of-Squares Speaker: Pravesh Kothari Affiliation: Member, School of Mathematics Date: Febuary 6, 2018 For more videos, please visit http://video.ias.edu
From playlist Mathematics
High dimensional estimation via Sum-of-Squares Proofs – D. Steurer & P. Raghavendra – ICM2018
Mathematical Aspects of Computer Science Invited Lecture 14.6 High dimensional estimation via Sum-of-Squares Proofs David Steurer & Prasad Raghavendra Abstract: Estimation is the computational task of recovering a ‘hidden parameter’ x associated with a distribution 𝒟_x, given a ‘measurem
From playlist Mathematical Aspects of Computer Science
Stanford CS330 Deep Multi-Task & Meta Learning - Bayesian Meta-Learning l 2022 I Lecture 12
For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai To follow along with the course, visit: https://cs330.stanford.edu/ To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu Chelsea Finn Computer
From playlist Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022
Christian-Yann Robert: Hill random forests with application to tornado damage insurance
CONFERENCE Recording during the thematic meeting : "MLISTRAL" the September 29, 2022 at the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathem
From playlist Probability and Statistics
Ravi Kumar - Random Walks and Graph Properties
https://indico.math.cnrs.fr/event/3475/attachments/2180/2572/Kumar_GomaxSlides.pdf
From playlist Google matrix: fundamentals, applications and beyond
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
The Normal Distribution (1 of 3: Introductory definition)
More resources available at www.misterwootube.com
From playlist The Normal Distribution