Intro to non normal distributions. Several examples including exponential and Weibull.
From playlist Probability Distributions
Overview of non probability sampling; advantages and disadvantages, types. 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
This lesson introduces the different sample methods when conducting a poll or survey. Site: http://mathispower4u.com
From playlist Introduction to Statistics
JUDGMENT and SNOWBALL Non-random Sampling (12-6)
Judgment sampling (a.k.a., expert sampling, authoritative sampling, purposive sampling, judgmental sampling) is a technique in which the sample is selected based on the researcher’s (or other experts’) existing knowledge or professional judgment. It may provide highly accurate findings wit
From playlist Sampling Distributions in Statistics (WK 12 - QBA 237)
Variance (4 of 4: Proof of two formulas)
More resources available at www.misterwootube.com
From playlist Random Variables
Random and systematic error explained: from fizzics.org
In scientific experiments and measurement it is almost never possible to be absolutely accurate. We tend to make two types of error, these are either random or systematic. The video uses examples to explain the difference and the first steps you might take to reduce them. Notes to support
From playlist Units of measurement
Empirical Measures along FPP Geodesics by Erik Bates
PROGRAM FIRST-PASSAGE PERCOLATION AND RELATED MODELS (HYBRID) ORGANIZERS Riddhipratim Basu (ICTS-TIFR, India), Jack Hanson (City University of New York, US) and Arjun Krishnan (University of Rochester, US) DATE & TIME 11 July 2022 to 29 July 2022 VENUE Ramanujan Lecture Hall and online Th
From playlist First-Passage Percolation and Related Models 2022 Edited
Lec 21 | MIT 2.830J Control of Manufacturing Processes, S08
Lecture 21: Case study 3: spatial modeling Instructor: Duane Boning, David Hardt View the complete course at: http://ocw.mit.edu/2-830JS08 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 2.830J, Control of Manufacturing Processes S08
Random Sampling - Statistical Inference
In this video I talk about Random Sampling - I give you a full, in-depth primer about random sampling and what sampling is in general. I then discuss the two ways of taking a random sample from a population (1st way: No replacement; 2nd way: With replacement) and point out the difference b
From playlist Statistical Inference
(ML 7.10) Posterior distribution for univariate Gaussian (part 2)
Computing the posterior distribution for the mean of the univariate Gaussian, with a Gaussian prior (assuming known prior mean, and known variances). The posterior is Gaussian, showing that the Gaussian is a conjugate prior for the mean of a Gaussian.
From playlist Machine Learning
Conceptual Questions about Random Variables and Probability Distributions
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Conceptual Questions about Random Variables and Probability Distributions
From playlist Statistics
Breaking of Ensemble Equivalence in dense random graphs by Nicos Starreveld
Large deviation theory in statistical physics: Recent advances and future challenges DATE: 14 August 2017 to 13 October 2017 VENUE: Madhava Lecture Hall, ICTS, Bengaluru Large deviation theory made its way into statistical physics as a mathematical framework for studying equilibrium syst
From playlist Large deviation theory in statistical physics: Recent advances and future challenges
Giray Ökten: Derivative pricing, simulation from non-uniform distributions - lecture 3
The models of Bachelier and Samuelson will be introduced. Methods for generating number sequences from non-uniform distributions, such as inverse transformation and acceptance rejection, as well as generation of stochastic processes will be discussed. Applications to pricing options via re
From playlist Probability and Statistics
Deep Learning Lecture 11.1 - Variational Autoencoders
Introduction to directed Generative Networks Transformation of Random Variables Variational Autoencoders
From playlist Deep Learning Lecture
Gunther Leobacher: Quasi Monte Carlo Methods and their Applications
In the first part, we briefly recall the theory of stochastic differential equations (SDEs) and present Maruyama's classical theorem on strong convergence of the Euler-Maruyama method, for which both drift and diffusion coefficient of the SDE need to be Lipschitz continuous. VIRTUAL LECTU
From playlist Virtual Conference
Fabio Toninelli - Ising model, Glauber dynamics and random tilings
In this talk I will give a panorama of results for the zero-temperature Glauber dynamics of the 3-dimensional (classical) Ising model. It is well known that, with suitable Dobrushin-type boundary conditions, the Boltzmann-Gibbs distribution of a 3d Ising interface at zero temperature coinc
From playlist 100…(102!) Years of the Ising Model
DDPS | Uncertainty-aware guided wave structural health monitoring using ensemble learning
Uncertainty-aware guided wave structural health monitoring using ensemble learning by Ishan Khurjekar (University of Florida) Description: Monitoring the integrity of structures such as buildings, bridges, oilrigs, and airplanes among others, is crucial in today’s world. Indeed, the field
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Verifying The Unseen: Interactive Proofs for Label-Invariant Distribution Properties - Guy Rothblum
Computer Science/Discrete Mathematics Seminar I Topic: Verifying The Unseen: Interactive Proofs for Label-Invariant Distribution Properties Speaker: Guy Rothblum Affiliation: Weizmann Institute Date: October 4, 2021 Given i.i.d. samples drawn from an unknown distribution over a large do
From playlist Mathematics
Introduction to Random Variables
Introduction to random variables and probability distribution functions. More free lessons at: http://www.khanacademy.org/video?v=IYdiKeQ9xEI
From playlist Statistics