The Smith predictor (invented by O. J. M. Smith in 1957) is a type of predictive controller designed to control systems with a significant feedback time delay. The idea can be illustrated as follows. Suppose the plant consists of followed by a pure time delay . refers to the Z-transform of the transfer function relating the inputs and outputs of the plant . As a first step, suppose we only consider (the plant without a delay) and design a controller with a closed-loop transfer function that we consider satisfactory. Next, our objective is to design a controller for the plant so that the closed loop transfer function equals . Solving ,we obtain. The controller is implemented as shown in the following figure, where has been changed to to indicate that it is a model used by the controller. Note that there are two feedback loops. The outer control loop feeds the output back to the input, as usual. However, this loop alone would not provide satisfactory control, because of the delay; this loop is feeding back outdated information. Intuitively, for the k sample intervals during which no fresh information is available, the system is controlled by the inner loop which contains a predictor of what the (unobservable) output of the plant G currently is. To check that this works, a re-arrangement can be made as follows: Here we can see that if the model used in the controller, , matches the plant perfectly, then the outer and middle feedback loops cancel each other, and the controller generates the "correct" control action. In reality, however, it is impossible for the model to perfectly match the plant. (Wikipedia).
(PP 6.6) Geometric intuition for the multivariate Gaussian (part 1)
How to visualize the effect of the eigenvalues (scaling), eigenvectors (rotation), and mean vector (shift) on the density of a multivariate Gaussian.
From playlist Probability Theory
(PP 6.7) Geometric intuition for the multivariate Gaussian (part 2)
How to visualize the effect of the eigenvalues (scaling), eigenvectors (rotation), and mean vector (shift) on the density of a multivariate Gaussian.
From playlist Probability Theory
EXTRA MATH Lec 6B: Maximum likelihood estimation for the binomial model
Forelæsning med Per B. Brockhoff. Kapitler:
From playlist DTU: Introduction to Statistics | CosmoLearning.org
Probability: We define geometric random variables, and find the mean, variance, and moment generating function of such. The key tools are the geometric power series and its derivatives.
From playlist Probability
In this Wolfram Technology Conference presentation, Pradipto Ghosh and Bob Sandheinrich demonstrate a series of examples showing how to model, analyze, design, and simulate controls systems with Mathematica. To learn more about Mathematica, please visit: http://www.wolfram.com/mathematica
From playlist Wolfram Technology Conference 2012
Lenaïc Chizat - Analysis of Gradient Descent on Wide Two-Layer Neural Networks
Artificial neural networks are a class of "prediction" functions parameterized by a large number of parameters -- called weights -- that are used in various machine learning tasks (classification, regression, etc). Given a learning task, the weights are adjusted via a gradient-based algori
From playlist Journée statistique & informatique pour la science des données à Paris-Saclay 2021
Alasdair MacIntyre on the Sources of Unpredictability in Human Affairs (1972)
This lecture explicates an early form of the argument, developed in MacIntyre’s 1972 paper “Predictability and Explanation in the Social Sciences,” that becomes the central claim of chapter 8 of After Virtue. Namely, that generalizations in social science lack predictive power or value. Ma
From playlist Social & Political Philosophy
Python - Information Extraction Part 2 (2023 New)
Lecturer: Dr. Erin M. Buchanan Spring 2023 https://www.patreon.com/statisticsofdoom In this video, you will learn about information extraction: keyphrase extraction, named entity recognition/disambiguation, and relation extraction. You will learn about spacy, textacy, and more python p
From playlist Natural Language Processing
PSHSummit 2022 - PowerShell Tooling by Steven Bucher
PowerShell Summit videos are recorded on a "best effort" basis. We use a room mic to capture as much room audio as possible, with an emphasis on capturing the speaker. Our recordings are made in a way that minimizes overhead for our speakers and interruptions to our live audience. These re
From playlist PowerShell + DevOps Global Summit 2022
Analyses of gradient methods for the optimization of wide two layer by Lenaic Chizat
DISCUSSION MEETING : STATISTICAL PHYSICS OF MACHINE LEARNING ORGANIZERS : Chandan Dasgupta, Abhishek Dhar and Satya Majumdar DATE : 06 January 2020 to 10 January 2020 VENUE : Madhava Lecture Hall, ICTS Bangalore Machine learning techniques, especially “deep learning” using multilayer n
From playlist Statistical Physics of Machine Learning 2020
The State of the Shell by Jeffery Snover, Jason Helmick, Sydney Smith and Dave Martins
PowerShell Summit videos are recorded on a "best effort" basis. We use a room mic to capture as much room audio as possible, with an emphasis on capturing the speaker. Our recordings are made in a way that minimizes overhead for our speakers and interruptions to our live audience. These re
From playlist PowerShell + DevOps Global Summit 2022
Boris Beranger - Composite likelihood and logistic regression models for aggregated data
Dr Boris Beranger (UNSW Sydney) presents “Composite likelihood and logistic regression models for aggregated data”, 14 August 2020. This seminar was organised by the University of Technology Sydney.
From playlist Statistics Across Campuses
Ex: Calculate the Sample Standard Deviation
This video explains how to calculator the sample standard deviation of a data set. http://mathispower4u.com
From playlist Statistics: Describing Data
(ML 7.7.A2) Expectation of a Dirichlet random variable
How to compute the expected value of a Dirichlet distributed random variable.
From playlist Machine Learning
Statistics 5_1 Confidence Intervals
In this lecture explain the meaning of a confidence interval and look at the equation to calculate it.
From playlist Medical Statistics
Sudipto Banerjee: High-dimensional Bayesian geostatistics
Abstract: With the growing capabilities of Geographic Information Systems (GIS) and user-friendly software, statisticians today routinely encounter geographically referenced data containing observations from a large number of spatial locations and time points. Over the last decade, hierarc
From playlist Probability and Statistics
From playlist STAT 501
Chapter 12 Sensitivity Specificity Predictive Values Odds Ratios
Ever wandered how to calculate sensitivity, specificity, positive and negative predictive values or odds ratios or even simply what these terms mean? Watch this short lecture.
From playlist Medical Statistics
Professor Mike West: Structured Dynamic Graphical Models & Scaling Multivariate Time Series
The Turing Lectures - Professor Mike West: Structured Dynamic Graphical Models & Scaling Multivariate Time Series. Click the below timestamps to navigate the video. 00:00:12 Welcome & Introduction by Doctor Ioanna Manolopoulou 00:01:19 Professor Mike West: Structured Dynamic
From playlist Turing Lectures
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