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18:59 MucAct COPD SIV SOP Training Alice Graves 13th… MucAct COPD SIV SOP Training Alice Graves 13th July 2021	MucAct COPD SIV SOP Training Alice Graves 13th July 2021 MucAct COPD SIV SOP Training Alice Graves 13th July 2021 sop training study trial sponsor protocol team consent safety source participant place deviation event imp events log pi 's pharmacovigilance document From Emma Ward on July 14th, 2021 0 likes 11 plays 0 Licence Type All rights reserved Language English Date Created July 13th, 2021
14:55 Spatial Extremes: Silius M. Vandeskog Spatial Extremes: Silius M. Vandeskog	Spatial Extremes: Silius M. Vandeskog This talk has been automatically captioned. You can remove these by pressing CC on the video toolbar. Name: Silius M. Vandeskog Talk Title: Modelling extreme sub-daily precipitation with the… eva distribution parameter tail precipitation model location inference parameters levels return priors space gumbel maxima deviation mixing alpha cdf scale quantiles From Belle Taylor on June 29th, 2021 0 likes 24 plays 0 Publisher Centre for Statistics Licence Type All rights reserved Language English Date Created June 29th, 2021
03:00 Olivier Wintenberger EVA Talk Preview Olivier Wintenberger EVA Talk Preview	Olivier Wintenberger EVA Talk Preview This talk has captions. You can remove these by pressing CC on the video toolbar. Name: Olivier Wintenberger Talk Title: Threshold selection for cluster inference based on large deviation… eva icms model data theory solution selection cluster large deviation principles blocks process alpha norm clustering respect infinity approach lp threshold limit analyses bias deviations study statistics apple choice From Anna Munro on May 25th, 2021 0 likes 22 plays 0 Licence Type All rights reserved Language English
03:16 Igor Rodionov EVA Talk Preview Igor Rodionov EVA Talk Preview	Igor Rodionov EVA Talk Preview This talk has captions. You can remove these by pressing CC on the video toolbar. Name: Igor Rodionov Talk Title: Precise large deviations for m-dependent subexponential sequences Abstract: In… icms eva solution theory precise large deviations m-dependent subexpotential sequences data result distribution seconds function probability sequence station division condition matrices covariance sample talk entries limit deviation polar f. session From Anna Munro on May 24th, 2021 0 likes 14 plays 0 Licence Type All rights reserved Language English
02:55 Sebastian Neblung EVA Talk Preview Sebastian Neblung EVA Talk Preview	Sebastian Neblung EVA Talk Preview This talk has captions. You can remove these by pressing CC on the video toolbar. Name: Sebastian Neblung Talk Title: Cluster based estimator for the spectral tail process Abstract: A new type of… theory solution icms eva neblung cluster estimator spectral tail process distribution projection estimation function error transform suspect tape series talk x. deviation performance poses property spec epa method From Anna Munro on May 13th, 2021 0 likes 13 plays 0 Licence Type All rights reserved Language English
11:54 dapr1 - making decisions (c) dapr1 - making decisions (c)	dapr1 - making decisions (c) Part C uoepsy sample distribution hypothesis body values degrees curve temperature deviation celsius humans significance difference alpha proportion density error standard evidence temp From Umberto Noe on February 7th, 2021 0 likes 233 plays 0 Licence Type All rights reserved The University of Edinburgh
13:26 dapr1 - hypothesis testing: critical values (e) dapr1 - hypothesis testing: critical values (e)	dapr1 - hypothesis testing: critical values (e) Part E uoepsy distribution sample score error deviation standard hypothesis approach units values errors minus column quantiles root bar sigma exercises quanta denominator From Umberto Noe on January 24th, 2021 0 likes 226 plays 0 Licence Type All rights reserved The University of Edinburgh
14:03 dapr1 - hypothesis testing: critical values (b) dapr1 - hypothesis testing: critical values (b)	dapr1 - hypothesis testing: critical values (b) Part B uoepsy distribution sample bootstrap error standard effect hypothesis stroop deviation bar parameter column replacement root curve approach participants n. size pattern From Umberto Noe on January 24th, 2021 0 likes 298 plays 0 Licence Type All rights reserved The University of Edinburgh
11:51 dapr1 - hypothesis testing: critical values (a) dapr1 - hypothesis testing: critical values (a)	dapr1 - hypothesis testing: critical values (a) Part A uoepsy task effect difference sample stroop colour mismatching matching population column participant completion variable words colours times deviation columns word participants From Umberto Noe on January 24th, 2021 0 likes 306 plays 0 Licence Type All rights reserved The University of Edinburgh
21:06 dapr1 - hypothesis testing: critical values dapr1 - hypothesis testing: critical values	dapr1 - hypothesis testing: critical values Hypothesis testing using the critical value approach uoepsy sample probability distribution hypothesis population left values sigma mu statistic plot question quantiles parameter week significance evidence median deviation bootstrap From Umberto Noe on January 24th, 2021 0 likes 339 plays 0 Licence Type All rights reserved The University of Edinburgh
09:05 FDS-S2-01-2-3 Standard error FDS-S2-01-2-3 Standard error	FDS-S2-01-2-3 Standard error We introduce the standard error of an estimator and, in particular, the standard error of the mean. inf2-fds error standard distribution deviation estimator sigma root hat sample estimate variance replication samples n. estimates simulations size subscript thing parameter From David Sterratt on January 14th, 2021 0 likes 211 plays 0 Course Code INFR08030 Publisher David Sterratt Licence Type Creative Commons - Attribution Non Commercial Language English Date Created January 14th, 2021
09:31 FDS-S2-01-2-1 Introduction to estimation FDS-S2-01-2-1 Introduction to estimation	FDS-S2-01-2-1 Introduction to estimation We'll introduce the concept of estimating a population or distribution parameter from a sample. inf2-fds distribution population estimator parameters parameter sample lambda deviation pizzas point cheese populations confidence median centre hat theta bit notation intervals From David Sterratt on January 14th, 2021 0 likes 254 plays 0 Course Code INFR08030 Publisher David Sterratt Licence Type Creative Commons - Attribution Non Commercial Language English Date Created January 14th, 2021
48:51 Distillation lecture 4 2019 part 2 Distillation lecture 4 2019 part 2	Distillation lecture 4 2019 part 2 Distillation lecture 4 part 2 hydrogen vapour molecules bonding pressure bonds deviation donor class effect water molecule star things atoms composition maximum component times minimum From John Christy on December 31st, 2020 0 likes 75 plays 0 Course Code CHEE11023 Publisher John Christy Licence Type All rights reserved The University of Edinburgh Language British English Date Created December 31st, 2020
16:34 Priniciples of Neuropsychology: How do we know if… Priniciples of Neuropsychology: How do we know if someone has a deficit _ with captions	Priniciples of Neuropsychology: How do we know if someone has a deficit _ with captions deviation iq tests percentile neuropsychology From Sharon Abrahams on December 30th, 2020 0 likes 52 plays 0 Course Code PSYL11058 Licence Type All rights reserved Date Created December 31st, 2020
15:48 USMR lecture 8 part 1 USMR lecture 8 part 1	USMR lecture 8 part 1 Scaling usmr psychology ppls statistics rstats age model week intercept hours reading deviation effect change predictor axis outcome practice slope hour students standardization output fit function From Martin Corley on November 8th, 2020 0 likes 166 plays 0 Course Code PSYL11053 Licence Type Creative Commons - Attribution Language English Date Created November 8th, 2020
09:35 ML1: Representing your data ML1: Representing your data	ML1: Representing your data In the first video, we learn how we can represent data from a multitude of sources as vectors. We show that these vectors can be stacked into a matrix to represent an entire dataset, and the… matrix vector column point points row entry measurements dimensions amplitude width length deviation image images flower values dataset dimension columns From Elliot Crowley on November 3rd, 2020 0 likes 27 plays 0

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