Variance reduction | Monte Carlo methods
In mathematics, more specifically in the theory of Monte Carlo methods, variance reduction is a procedure used to increase the precision of the estimates obtained for a given simulation or computational effort. Every output random variable from the simulation is associated with a variance which limits the precision of the simulation results. In order to make a simulation statistically efficient, i.e., to obtain a greater precision and smaller confidence intervals for the output random variable of interest, variance reduction techniques can be used. The main ones are common random numbers, antithetic variates, control variates, importance sampling, stratified sampling, , and . For simulation with black-box models subset simulation and line sampling can also be used. Under these headings are a variety of specialized techniques; for example, particle transport simulations make extensive use of "weight windows" and "splitting/Russian roulette" techniques, which are a form of importance sampling. (Wikipedia).
Derivations.2.Derivation of Variance
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From playlist Optional - Derivations
Covariance (1 of 17) What is Covariance? in Relation to Variance and Correlation
Visit http://ilectureonline.com for more math and science lectures! To donate:a http://www.ilectureonline.com/donate https://www.patreon.com/user?u=3236071 We will learn the difference between the variance and the covariance. A variance (s^2) is a measure of how spread out the numbers of
From playlist COVARIANCE AND VARIANCE
Variance (4 of 4: Proof of two formulas)
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From playlist Random Variables
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Measures of Variation
From playlist Statistics
How to find the number of standard deviations that it takes to represent all the data
👉 Learn how to find the variance and standard deviation of a set of data. The variance of a set of data is a measure of spread/variation which measures how far a set of numbers is spread out from their average value. The standard deviation of a set of data is a measure of spread/variation
From playlist Variance and Standard Deviation
Variance on a Modified Distribution (2 of 2: Investigating the modifications)
More resources available at www.misterwootube.com
From playlist Random Variables
How to find the variance and standard deviation from a set of data
👉 Learn how to find the variance and standard deviation of a set of data. The variance of a set of data is a measure of spread/variation which measures how far a set of numbers is spread out from their average value. The standard deviation of a set of data is a measure of spread/variation
From playlist Variance and Standard Deviation
Learning how to find the variance and standard deviation from a set of data
👉 Learn how to find the variance and standard deviation of a set of data. The variance of a set of data is a measure of spread/variation which measures how far a set of numbers is spread out from their average value. The standard deviation of a set of data is a measure of spread/variation
From playlist Variance and Standard Deviation
Covariance (3 of 17) Population vs Sample Variance
Visit http://ilectureonline.com for more math and science lectures! To donate:a http://www.ilectureonline.com/donate https://www.patreon.com/user?u=3236071 We will learn the difference and calculate the variance of a population and the variance of a sample of a population. Next video in
From playlist COVARIANCE AND VARIANCE
Cross-Validation and Mean-Square Stability - Sergei Vassilvitskii
Sergei Vassilvitskii Yahoo! Research January 17, 2011 A popular practical method of obtaining a good estimate of the error rate of a learning algorithm is k-fold cross-validation. Here, the set of examples is first partitioned into k equal-sized folds. Each fold acts as a test set for eval
From playlist Mathematics
Power Spectrum Estimation Examples: Welch's Method
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Examples of applying Welch's method to estimate power spectrum highlighting the tradeoffs between bias and variance that are associated with s
From playlist Estimation and Detection Theory
Welch's Method: The Averaged Periodogram
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Poor variance properties of the periodogram motivate averaging methods for estimating the power spectrum. In Welch's method the data is partit
From playlist Estimation and Detection Theory
Decision Tree Regression in Python (from scratch!)
How about creating a decision tree regressor without using sci-kit learn? This video will show you how to code a decision tree to solve regression problems from scratch! #machinelearning #datascience #python For more videos please subscribe - http://bit.ly/normalizedNERD Love my work?
From playlist Tree-Based Algorithms
Data Analysis 6: Principal Component Analysis (PCA) - Computerphile
PCA - Principle Component Analysis - finally explained in an accessible way, thanks to Dr Mike Pound. This is part 6 of the Data Analysis Learning Playlist: https://www.youtube.com/playlist?list=PLzH6n4zXuckpfMu_4Ff8E7Z1behQks5ba This Learning Playlist was designed by Dr Mercedes Torres
From playlist Data Analysis with Dr Mike Pound
19 Data Analytics: Principal Component Analysis
Lecture on unsupervised machine learning with principal component analysis for dimensional reduction, inference and prediction.
From playlist Data Analytics and Geostatistics
Principal Component Analysis (PCA) - THE MATH YOU SHOULD KNOW!
In this video, we are going to see exactly how we can perform dimensionality reduction with a famous Feature Extraction technique - Principal Component Analysis PCA. We’ll get into the math that powers it REFERENCES [1] Computing Eigen vectors and Eigen values: https://www.scss.tcd.ie/~d
From playlist The Math You Should Know
08b Machine Learning: Principal Component Analysis
Lecture of principal component analysis for dimensionality reduction and general inference, learning about the structures in our subsurface data. Follow along with the demonstration workflow in Python's scikit-learn package: https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/
From playlist Machine Learning
Combining Means and Variance in Statistics PLEASE READ DESCRIPTION
I review how linear transformations affect the mean, standard deviation, and the variance of data. I then introduce the rules for combining the means and variance of mutliple sets of data and finish with an example. Check out http://www.ProfRobBob.com, there you will find my lessons orga
From playlist AP Statistics
15 Machine Learning: Random Forest
Lecture on machine learning with ensemble tree methods. Tree bagging and random forest to mitigate against the model variance component of model inaccuracy in testing! Follow along with the demonstration in Python: https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/Subsurface
From playlist Machine Learning