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19:11 Selection on a single trait in AlphaSimR - Part 1 Selection on a single trait in AlphaSimR - Part 1	Selection on a single trait in AlphaSimR - Part 1 In this vignette, we will learn about simulating selection to improve a single trait and explore some of the intricacies of the breeder’s equation. (Part 1) population phenotype individuals values base phenotypes parents selection function histogram generation units response progeny variance average heritability pop dataframe parameters From Gregor Gorjanc 0 likes 22 plays 0 Licence Type Creative Commons - Attribution Share A Like
52:30 Year 3, Week 6 Group Meeting (27 October 2021) Year 3, Week 6 Group Meeting (27 October 2021)	Year 3, Week 6 Group Meeting (27 October 2021) Year group meeting for 3rd year HCA students, semester 1, week 6. Dr Alasdair Raffe (Deputy Undergraduate Director)Caroline Stirling (HCA Academic Librarian)Lyn Kane (HCA Careers Service Advisor) year group meetings hca library experience credits circumstances databases students skills average courses degree questions class things application lot dissertation slide marks internship resources From Muireann Crowley 0 likes 29 plays 0 Licence Type All rights reserved The University of Edinburgh Language English Date Created October 27th, 2021
01:35 Joint surface detection and depth estimation from… Joint surface detection and depth estimation from single-photon Lidar data using ensemble estimators - Kris Drummond	Joint surface detection and depth estimation from single-photon Lidar data using ensemble estimators - Kris Drummond SSPD 2021 Poster Lightning Presentation SessionPresenter: Kristofer DrummondPoster Title: Joint surface detection and depth estimation from single-photon Lidar data using ensemble… sspd 2021 problem model marginalisation variables poster quantification uncertainty concern applications reconstruction harm advantages benefits lot average slash selection inference button competition From Joe Burchell 0 likes 12 plays 0 Licence Type All rights reserved Language English Date Created October 14th, 2021
39:41 Welcome Back: Year 4 Mechanical Engineering… Welcome Back: Year 4 Mechanical Engineering programmes	Welcome Back: Year 4 Mechanical Engineering programmes Welcome Back: Year 4 Mechanical Engineering programmes school of engineering induction 2021 things students courses tutor semester circumstances week student credits degree lot end career person percent average classification questions permission studies From Eddie Dubourg 0 likes 7 plays 0 Publisher School of Engineering Licence Type All rights reserved The University of Edinburgh Date Created September 16th, 2021 Retain Source File Yes
38:31 Welcome week: Finalists Welcome Back Meeting Welcome week: Finalists Welcome Back Meeting	Welcome week: Finalists Welcome Back Meeting School of History, Classics and Archaeology Welcome back to all 4th year UG students With Dr Ewen Cameron and Dr Catriona Pickard hca students feedback student slide dissertation study meeting skills marks edinburgh opportunity survey average tutor degree things bit months questions career From Muireann Crowley 0 likes 18 plays 0 Publisher The University of Edinburgh Licence Type All rights reserved The University of Edinburgh
15:37 Machine Learning for extremes: Chen Zhou Machine Learning for extremes: Chen Zhou	Machine Learning for extremes: Chen Zhou This talk has been automatically captioned. You can remove these by pressing CC on the video toolbar. Name: Chen Zhou Talk Title: Distributed Inference for Tail Empirical and Quantile… eva process tail oracle estimator function machine weight root result machines sample errors proof property infinity estimators statistics average convergence distribution From Belle Taylor 0 likes 72 plays 0 Publisher Centre for Statistics Licence Type All rights reserved Language English Date Created June 29th, 2021
17:32 Climate Extremes: Jordan Richards Climate Extremes: Jordan Richards	Climate Extremes: Jordan Richards This talk has been automatically captioned. You can remove these by pressing CC on the video toolbar. Name: Jordan Richards Talk Title: Modelling the extremes of spatial aggregates of precipitation… eva process model condition functions dependence function margins side approach bit distance variable regions estimates delta paper inference domain average locations From Belle Taylor 0 likes 70 plays 0 Publisher Centre for Statistics Licence Type All rights reserved Language English Date Created June 29th, 2021
07:03 Selection and Response to Selection Selection and Response to Selection	Selection and Response to Selection We will look at selective breeding, demonstrating how it can improve a population, and predicting a possible response to selection. The essence in selective breeding is to identify genetically best… insilico breeding selection eggs response females generation population equation males variation dna heritability phenotype breeder differential generations simulations x. values distribution average breeding alphasimr From Geoff Fortescue 2 likes 50 plays 0 Licence Type Creative Commons - Attribution Share A Like
55:57 UK-APASI in Mathematical Sciences: Fred Vermolen UK-APASI in Mathematical Sciences: Fred Vermolen	UK-APASI in Mathematical Sciences: Fred Vermolen Subtitles will be added soon. May 2021 Fred Vermolen, Introductory statistics and the central limit theorem population sample statistics men height probability englishman length cetera average variance entries question thing times parameters sampling median properties minus From Liam Holligan 0 likes 8 plays 0 Publisher Liam Holligan Licence Type All rights reserved Language English Date Created May 26th, 2021
03:51 Chen Zhou EVA Talk Preview Chen Zhou EVA Talk Preview	Chen Zhou EVA Talk Preview This talk has captions. You can remove these by pressing CC on the video toolbar. Name: Chen Zhou Talk Title: Distributed Inference for Tail Empirical and Quantile Processes Abstract: The… solution theory eva icms zhou distributed inference tail empirical quantile processes oracle estimator property process sample talk statistics machine maxima machines estimators goal divide infinity average result algorithm procedure From Anna Munro 0 likes 41 plays 0 Licence Type All rights reserved Language English
18:33 REF2020: Pedro Augusto Agostini- Particle… REF2020: Pedro Augusto Agostini- Particle production beyond eikonal accuracy in dilute-dense CGC framework	REF2020: Pedro Augusto Agostini- Particle production beyond eikonal accuracy in dilute-dense CGC framework REF2020: Pedro Augusto Agostini- Particle production beyond eikonal accuracy in dilute-dense CGC framework higgs centre re2020 proton target result production icon field functions model nucleus corrections parameter operator equation kind calculation question size energies function average From Lyndsey Ballantyne 0 likes 4 plays 0 Licence Type All rights reserved The University of Edinburgh Date Created December 9th, 2020
25:02 IDS - Week 11 - Code along IDS - Week 11 - Code along	IDS - Week 11 - Code along Wrangling and visualising data on the course videos data science rstats week things videos words plot names length thing function package variable title column average lengths bit text word total dataframe From Mine Cetinkaya-Rundel 0 likes 14 plays 0 Course Code MATH08077 Licence Type Creative Commons - Attribution Non Commercial Share A Like
26:59 Topic 65: Time Averages and Ergodicity (PETARS,… Topic 65: Time Averages and Ergodicity (PETARS, Chapter 8)	Topic 65: Time Averages and Ergodicity (PETARS, Chapter 8) This Topic introduces the notion of estimating statistical averages from a single realisation of a stochastic process. This concept is most easily developed for estimating first and second moments… pgee11164 average process ensemble variance ergodicity realisation infinity sense minus covariance function realisations chapter processes term samples sequence estimator sample estimation From James Hopgood 0 likes 203 plays 0 Course Code PGEE11164 Licence Type All rights reserved Date Created November 12th, 2020
03:47 MLP Lecture 05 - Clip 06 - RMSProp MLP Lecture 05 - Clip 06 - RMSProp	MLP Lecture 05 - Clip 06 - RMSProp Machine Learning Practical (MLP) Lecture 05, Clip 06 / 07. machine learning deep learning gradient learning beta average sum descent batch gradients networks ziggurats beginning times hyperparameters prop rms magnitude weight idea rate graph From Pavlos Andreadis 1 likes 356 plays 0 Course Code INFR11132 Licence Type All rights reserved Language English
03:17 MLP Lecture 05 - Clip 04 - Momentum MLP Lecture 05 - Clip 04 - Momentum	MLP Lecture 05 - Clip 04 - Momentum Machine Learning Practical (MLP) Lecture 05, Clip 04 / 07. machine learning deep learning gradients momentum history average idea optimization direction iteration update wi descent gradient velocity loss stability bit space weight eye sense From Pavlos Andreadis 1 likes 420 plays 0 Course Code INFR11132 Licence Type All rights reserved Language English
03:09 Getting variables from the SRM data Getting variables from the SRM data	Getting variables from the SRM data How to get key variables from the data collected by the SRM ergometer srm selection rate heart power window peak bottom tag pieces minute beats figures corner hand interest point cursor points average test From Jon Kelly 0 likes 6 plays 0 Licence Type Creative Commons - Attribution Non Commercial No Derivatives

Search for tag: "average"

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