Line sampling is a method used in reliability engineering to compute small (i.e., rare event) failure probabilities encountered in engineering systems. The method is particularly suitable for high-dimensional reliability problems, in which the performance function exhibits moderate non-linearity with respect to the uncertain parameters The method is suitable for analyzing black box systems, and unlike the importance sampling method of variance reduction, does not require detailed knowledge of the system. The basic idea behind line sampling is to refine estimates obtained from the first-order reliability method (FORM), which may be incorrect due to the non-linearity of the limit state function. Conceptually, this is achieved by averaging the result of different FORM simulations. In practice, this is made possible by identifying the importance direction in the input parameter space, which points towards the region which most strongly contributes to the overall failure probability. The importance direction can be closely related to the center of mass of the failure region, or to the failure point with the highest probability density, which often falls at the closest point to the origin of the limit state function, when the random variables of the problem have been transformed into the standard normal space. Once the importance direction has been set to point towards the failure region, samples are randomly generated from the standard normal space and lines are drawn parallel to the importance direction in order to compute the distance to the limit state function, which enables the probability of failure to be estimated for each sample. These failure probabilities can then be averaged to obtain an improved estimate. (Wikipedia).
What is quota sampling? Advantages and disadvantages. General steps and an example of how to find a quote sample. 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.creator-spring.
From playlist Sampling
Frequency Domain Interpretation of Sampling
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Analysis of the effect of sampling a continuous-time signal in the frequency domain through use of the Fourier transform.
From playlist Sampling and Reconstruction of Signals
Statistics: Introduction (12 of 13) Sampling: Definitions and Terms
Visit http://ilectureonline.com for more math and science lectures! We will review a sampling of definitions and terms of statistics: census, sampling frame, sampling plan, judgment sample, probability samples, random samples, systematic sample, stratified sample, and cluster sample. To
From playlist STATISTICS CH 1 INTRODUCTION
An overview of the most popular sampling methods used in statistics. 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.creator-spring.com/listing/sampling-in-statistics
From playlist Sampling
Statistics - Types of sampling
This video will show you the many ways that you could sample. Remember to look for those small differences such as if you are breaking things into groups first. For more videos visit http://www.mysecretmathtutor.com
From playlist Statistics
What is purposive (deliberate) sampling? Types of purposive sampling, advantages and disadvantages. 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.creator-spring.com/listing/sam
From playlist Sampling
What is systematic sampling? Advantages and disadvantages. How to perform systematic sampling and repeated systematic sampling. 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.c
From playlist Sampling
Statistics Lesson #1: Sampling
This video is for my College Algebra and Statistics students (and anyone else who may find it helpful). It includes defining and looking at examples of five sampling methods: simple random sampling, convenience sampling, systematic sampling, stratified sampling, cluster sampling. We also l
From playlist Statistics
If you’re studying a large population, you might consider using #sampling in order to get the data you need. We’ll explain how to come up with a proportionate, representative sample. To learn more basic concepts in #statistics, check out the free tutorial on our website: https://edu.gcfglo
From playlist Basic Statistics
Statistics for Data Science & Machine Learning
The most in demand skills in the world right now are in Data Science & Machine Learning! In this one video I will teach you a key part of Machine Learning and Data Science which is Statistics. I took everything in a standard 500 page text book on Statistics and put it in this one video. I
From playlist Machine Learning & Data Science
Statistical Rethinking 2022 Lecture 02 - Bayesian Inference
Bayesian updating, sampling posterior distributions, computing posterior and prior predictive distributions Course materials: https://github.com/rmcelreath/stat_rethinking_2022 Intro music: https://www.youtube.com/watch?v=QH_VKWStK98 Chapters: 00:00 Introduction 04:53 Garden of forking
From playlist Statistical Rethinking 2022
Structured Regularization Summer School - C. Boyer - 22/06/2017
Claire Boyer (UPMC) Towards realistic compressed sensing Abstract: First, we will theoretically justify the applicability of compressed sensing (CS) in real-life applications. To do so, CS theorems compatible with physical acquisition constraints will be presented. These new results do n
From playlist Structured Regularization Summer School - 19-22/06/2017
Texture Sampling #2: Bilinear & Bicubic Samples
Following on from Part 1, I look at two common sampling methods that aim to reduce artefacts, bilinear and bicubic point sampling. The former linearly interpolates between neighbouring pixels, and the latter uses even further pixels to bias cubic splines to give a smoother contour between
From playlist Interesting Programming
EstimatingRegressionCoeff.7.Unbiased
This video is brought to you by the Quantitative Analysis Institute at Wellesley College. The material is best viewed as part of the online resources that organize the content and include questions for checking understanding: https://www.wellesley.edu/qai/onlineresources
From playlist Estimating Regression Coefficients
The DISTRIBUTION of Sample Means with Stats Blocks (11-4)
The Distribution of Sample Means (DSM) is all of the sample means for all of the possible random samples of a particular sample size (n) that can be obtained from a population. The distribution of sample means becomes more normally distributed with larger sample sizes. Dr. Daniel uses Stat
From playlist Sampling And Populations in Statistics (WK 11 - QBA 237)
Statistical Rethinking 2022 Lecture 07 - Overfitting
Slides and other course materials: https://github.com/rmcelreath/stat_rethinking_2022 Music: Intro: https://www.youtube.com/watch?v=R9bwnY05GoU Pause: https://www.youtube.com/watch?v=wAPCSnAhhC8 Chapters: 00:00 Introduction 04:26 Problems of prediction 07:00 Cross-validation 22:00 Regula
From playlist Statistical Rethinking 2022
Statistical Rethinking - Lecture 04
Lecture 04, Linear Models, from Statistical Rethinking, A Bayesian Course with R Examples
From playlist Statistical Rethinking Winter 2015
Probability and non-probability sampling
In this video, Professor Matthew Salganik discusses probability and non-probability sampling for survey research in the digital age. Link to slides: https://github.com/compsocialscience/summer-institute/blob/master/2020/materials/day4-surveys/02-nonprobability-sampling.pdf Links to other m
From playlist SICSS 2020
Quantization and Coding in A/D Conversion
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Real sampling systems use a limited number of bits to represent the samples of the signal, resulting in quantization of the signal amplitude t
From playlist Sampling and Reconstruction of Signals