Search for tag: "naught"

16 Media

43:14 UK-APASI in Mathematical Sciences: John H.… UK-APASI in Mathematical Sciences: John H. Njagarah	UK-APASI in Mathematical Sciences: John H. Njagarah Subtitles will be added soon. Wednesday 24 February 2021 UK-APASI in Mathematical Sciences John H. Njagarah, Botswana International University - Sensitivity analysis of parameters of an epidemic… parameters values sensitivity parameter drug epidemic model analysis reproduction output respect influence individuals users change population cases isolation naught measure From Liam Holligan on February 24th, 2021 0 likes 9 plays 0 Publisher Liam Holligan Licence Type Creative Commons - Attribution Language English Date Created February 24th, 2021
09:22 Dong_part1 Dong_part1	Dong_part1 Part 1 (of 3) of talk showing some examples of event attribution. Talk developed for virtual workshop in Event Attribution for early career China-based researchers. event attribution workshop china event attribution presentation water probability risk events threshold climate change summer heat human ratio naught literature focus stimulation From Simon Tett on February 16th, 2021 0 likes 22 plays 0 Publisher Buwen Dong Licence Type All rights reserved Language English Date Created February 16th, 2021
59:19 One World Virtual Seminar Series - Stochastic… One World Virtual Seminar Series - Stochastic Numerics and Inverse Problems: Denis Talay (Inria and Ecole Polytechnique)	One World Virtual Seminar Series - Stochastic Numerics and Inverse Problems: Denis Talay (Inria and Ecole Polytechnique) Denis Talay (Inria and Ecole Polytechnique) Probability distributions of first hitting times of solutions to SDEs w.r.t. the Hurst parameter of the driving fractional Brownian noise: A sensitivity… icms brownian probability analysis sdes capital times formula expectation delta minus laplace respect power term function process operator transform trace star inequality diffusion naught motion From DIANE HORBERRY on February 10th, 2021 0 likes 33 plays 0 Publisher Diane Hoberry Licence Type All rights reserved Language English Date Created February 3rd, 2021
18:31 FDS-S2-02-2-5 Maximum likelihood estimation of… FDS-S2-02-2-5 Maximum likelihood estimation of logistic regression coefficients	FDS-S2-02-2-5 Maximum likelihood estimation of logistic regression coefficients We introduce the principle of maximum likelihood, and show how to derive the likelihood as a function of the coefficients for logistic regression. We also mention one topical application of logistic… inf2-fds beta log likelihood probability regression term failure function i. success sum naught principle coefficients point respect x. dataset equals thing From David Sterratt on January 20th, 2021 0 likes 125 plays 0 Course Code INFR08030 Publisher David Sterratt Licence Type Creative Commons - Attribution Non Commercial No Derivatives Language English Date Created January 20th, 2021
17:50 FDS-S2-02-2-1 Principle of logistic regression FDS-S2-02-2-1 Principle of logistic regression	FDS-S2-02-2-1 Principle of logistic regression We introduce the principle of logisitc regression, illustrating its application to a credit approval dataset. We also introduce the concepts of odds and odds ratios. inf2-fds regression odds probability beta approval success x. age variable function coefficients variables credit ratio minus curve classification naught linear times From David Sterratt on January 19th, 2021 0 likes 185 plays 0 Course Code INFR08030 Publisher David Sterratt Licence Type Creative Commons - Attribution Non Commercial No Derivatives Language English Date Created January 19th, 2021
17:41 FDS-S2-02-1-2 P-values FDS-S2-02-1-2 P-values	FDS-S2-02-1-2 P-values We introduce P-values, an important but tricky concept in hypothesis testing inf2-fds values hypothesis distribution naught bit probability statement simulations level rejection incompatibility binomial point topic truth chance points paper model definition From David Sterratt on January 18th, 2021 0 likes 185 plays 0 Course Code INFR08030 Publisher David Sterratt Licence Type Creative Commons - Attribution Non Commercial Language English Date Created January 17th, 2021
54:06 26 November Stahl-Totik regularity for orthogonal… 26 November Stahl-Totik regularity for orthogonal rational functions with real poles and GMP matrices Benjamin Eichinge	26 November Stahl-Totik regularity for orthogonal rational functions with real poles and GMP matrices Benjamin Eichinge An automated programme has been used to generate the subtitles on this talk. icms opsfota function point infinity measure problem inequality sequence green limit naught functions gap theorem polynomials pole poles regularity points alternation riemann From GILLIAN KERR on November 27th, 2020 0 likes 20 plays 0 Course Code ICMS Publisher ICMS Licence Type All rights reserved Language English Date Created November 27th, 2020
04:14 CL - 9d - DPLL III CL - 9d - DPLL III	CL - 9d - DPLL III We introduce the idea of using a divide and conquer algorithm. inf1a introduction to computation informatics 1 computation and logic gamma problem ways beginning naught left 's choices eye rule things literals truancy table rho staff wages irr micro cf From Haoran Peng on November 4th, 2020 0 likes 242 plays 0 Course Code INFR08025 Licence Type All rights reserved The University of Edinburgh Language English
18:39 Topic 49: Least Squares Estimation (PETARS,… Topic 49: Least Squares Estimation (PETARS, Chapter 6)	Topic 49: Least Squares Estimation (PETARS, Chapter 6) The least squares approach is presented as a non-probabilistic method for designing an estimator of a set of parameters, assuming a model is provided for describing the data. This is presented as… pgee11164 function squares model signal linear error estimator problem cost approach likelihood respect sum probability estimate term density parameter naught times From James Hopgood on October 28th, 2020 0 likes 63 plays 0 Course Code PGEE11164 Licence Type All rights reserved Date Created October 28th, 2020
02:29 CL - 6e - DNF CL - 6e - DNF	CL - 6e - DNF More on blocks of ones (which correspond to conjunctions of literals).This video shows how to use a Karnaugh map to represent an arbitrary Boolean function of four variables as a disjunctive normal… inf1a introduction to computation informatics 1 computation and logic karnaugh map blocks 's form disjunction block pattern literals things conjunctions r. expression naught aig b. zeros rows g. column piece power From Haoran Peng on October 24th, 2020 0 likes 398 plays 0 Course Code INFR08025 Licence Type All rights reserved The University of Edinburgh Language English
02:41 CL - 6c - Karnaugh Maps II CL - 6c - Karnaugh Maps II	CL - 6c - Karnaugh Maps II This video shows you how to find your way through the state space represented by a Karnaugh Map, and introduces features called blocks. In this video we focus on blocks of ones. inf1a introduction to computation informatics 1 computation and logic karnaugh map block variables dimensions row thing copies side blocks naught cube red amber things column reds rules edges picture sense place From Haoran Peng on October 24th, 2020 0 likes 405 plays 0 Course Code INFR08025 Licence Type All rights reserved The University of Edinburgh Language English
02:40 CL - Review 1 - Contraposition CL - Review 1 - Contraposition	CL - Review 1 - Contraposition We show the intuition of contraposition using Venn diagrams. inf1a introduction to computation informatics 1 computation and logic contraposition things b. diagram x. universe sequence rule hobby naught symbol complement satisfies notation predicates relationship holes function holds thing property From Haoran Peng on October 20th, 2020 0 likes 338 plays 0 Course Code INFR08025 Licence Type All rights reserved The University of Edinburgh Language English
23:54 Topic 34: Statistical Description of Random… Topic 34: Statistical Description of Random Vectors (PETARS, Chapter 5)	Topic 34: Statistical Description of Random Vectors (PETARS, Chapter 5) This video extends the concept of statistical descriptors of probability density functions to random vectors or multiple random variables. It introduces the concept of correlation, and how this… pgee11164 variables vector pdf correlation times moment probability density function moments dot values element bit matrix elements distribution x. naught expectation From James Hopgood on October 12th, 2020 0 likes 108 plays 0 Course Code PGEE11164 Licence Type All rights reserved Date Created October 12th, 2020
13:47 Topic 13: Bertrand’s Paradox (PETARS,… Topic 13: Bertrand’s Paradox (PETARS, Chapter 3)	Topic 13: Bertrand’s Paradox (PETARS, Chapter 3) This video highlights key difficulties with the classical definition of probability. It uses Bertrand's paradox as a problem in which to study the problems associated with classical… pgee11164 probability circle point definition event proportionality outcomes method radius problem ratio length events triangle ab naught r. paradox fact n. From James Hopgood on September 4th, 2020 0 likes 110 plays 0 Course Code PGEE11164 Licence Type All rights reserved Date Created September 4th, 2020
17:09 30a 30a	30a Markov Decision Processes: Representation decision reward action function transition chance problem model probability utility actions minus process nature network naught tree nodes point thing From Alexandra Lascarides on July 28th, 2020 0 likes 73 plays 0 Licence Type All rights reserved The University of Edinburgh
19:21 22a 22a	22a Joint Probability Distributions probability cavity toothache sum table variables catch distribution marginalization point values events vector alpha naught query evidence proposition probabilities element From Alexandra Lascarides on July 10th, 2020 0 likes 141 plays 0 Licence Type All rights reserved The University of Edinburgh

Search for tag: "naught"

the university of edinburgh