Numerical analysis | Computational statistics | Mathematics of computing
Computational statistics, or statistical computing, is the bond between statistics and computer science. It means statistical methods that are enabled by using computational methods. It is the area of computational science (or scientific computing) specific to the mathematical science of statistics. This area is also developing rapidly, leading to calls that a broader concept of computing should be taught as part of general statistical education. As in traditional statistics the goal is to transform raw data into knowledge, but the focus lies on computer intensive statistical methods, such as cases with very large sample size and non-homogeneous data sets. The terms 'computational statistics' and 'statistical computing' are often used interchangeably, although Carlo Lauro (a former president of the International Association for Statistical Computing) proposed making a distinction, defining 'statistical computing' as "the application of computer science to statistics",and 'computational statistics' as "aiming at the design of algorithm for implementingstatistical methods on computers, including the ones unthinkable before the computerage (e.g. bootstrap, simulation), as well as to cope with analytically intractable problems" [sic]. The term 'Computational statistics' may also be used to refer to computationally intensive statistical methods including resampling methods, Markov chain Monte Carlo methods, local regression, kernel density estimation, artificial neural networks and generalized additive models. (Wikipedia).
This branch of math can help you to organize and interpret information. It’s used in a variety of fields, and it has many applications in daily life. To learn more basic concepts in #statistics, check out the free tutorial on our website: https://edu.gcfglobal.org/en/statistics-basic-conce
From playlist Basic Statistics
Computing Statistics - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Statistics Lecture 3.3: Finding the Standard Deviation of a Data Set
https://www.patreon.com/ProfessorLeonard Statistics Lecture 3.3: Finding the Standard Deviation of a Data Set
From playlist Statistics (Full Length Videos)
This lecturelet will introduce you to the series on statistical analyses of time-frequency data. For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/
From playlist OLD ANTS #8) Statistics
Lecture01 Introduction to this course on medical statistics
A new course in medical statistics using widely available spreadsheet software.
From playlist Medical Statistics
Linear regression is used to compare sets or pairs of numerical data points. We use it to find a correlation between variables.
From playlist Learning medical statistics with python and Jupyter notebooks
Overview of logistic regression, a statistical classification technique.
From playlist Machine Learning
The mother of all representer theorems for inverse problems & machine learning - Michael Unser
This workshop - organised under the auspices of the Isaac Newton Institute on “Approximation, sampling and compression in data science” — brings together leading researchers in the general fields of mathematics, statistics, computer science and engineering. About the event The workshop ai
From playlist Mathematics of data: Structured representations for sensing, approximation and learning
Statistics Lecture 5.2: A Study of Probability Distributions, Mean, and Standard Deviation
https://www.patreon.com/ProfessorLeonard Statistics Lecture 5.2: A Study of Probability Distributions, Mean, and Standard Deviation
From playlist Statistics (Full Length Videos)
Lect.11D: Statistics For Intercept And Slope Lecture 11
Lecture with Per B. Brockhoff. Lecture 11. Chapters: 00:00 - Introduction; 01:15 - Inferences For The Regression Model; 10:30 - Confidence Intervals For Alpha And Beta;
From playlist DTU: Introduction to Statistics | CosmoLearning.org
Table of Contents: 00:50 - Lecture structure Two Proportions 01:11 - Checking assumptions 02:50 - Computing the standard error by hand 03:59 - Example: Computing the standard error for a confidence interval 06:22 - Example: Computing the standard error for a hypothesis test 08
From playlist STAT 200 Video Lectures
Quentin Berthet - Trade-offs in Statistical Learning
I will explore the notion of constraints on learning procedures, and discuss the impact that they can have on statistical precision. This is inspired by real-life concerns such as limits on time for computation, on reliability of observations, or communication b
From playlist Schlumberger workshop - Computational and statistical trade-offs in learning
Selecting the BEST Regression Model (Part C)
Regression Analysis by Dr. Soumen Maity,Department of Mathematics,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in
From playlist IIT Kharagpur: Regression Analysis | CosmoLearning.org Mathematics
Matthias Reitzner: Poisson U statistics Subgraph and Component Counts in Random Geometric Graphs
The lecture was held within the framework of the Hausdorff Trimester Program : Applied and Computational Algebraic Topology
From playlist HIM Lectures: Special Program "Applied and Computational Algebraic Topology"
Selecting the BEST Regression Model (Part D)
Regression Analysis by Dr. Soumen Maity,Department of Mathematics,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in
From playlist IIT Kharagpur: Regression Analysis | CosmoLearning.org Mathematics
0:55 - Review #1: Frequency tables 1:27 - Review #2: Two-way contingency tables 2:24 - Review #3: Probability distribution plots 3:26 - Review #4: Conditional probabilities 5:14 - Review #5: Independence 6:08 - Lesson 11 learning objectives 6:38 - 1. Construct a chi-square probability dist
From playlist STAT 200 Video Lectures
Professor Gareth Roberts: "New challenges in Computational Statistics"
The Turing Lectures: Statistics - Professor Gareth Roberts, University of Warwick “New challenges in Computational Statistics” Click the below timestamps to navigate the video. 00:00:09 Welcome by Professor Patrick Wolfe 00:01:44 Introduction by Professor Sofia Olhede 00:03:2
From playlist Turing Lectures
SketchySVD - Joel Tropp, California Institute of Technology
This workshop - organised under the auspices of the Isaac Newton Institute on “Approximation, sampling and compression in data science” — brings together leading researchers in the general fields of mathematics, statistics, computer science and engineering. About the event The workshop ai
From playlist Mathematics of data: Structured representations for sensing, approximation and learning
Statistical Learning: 13.6 Resampling Approaches II
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning