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The subtitles/captions on this talk are being edited. They
will be available within 2 weeks of the talk being published. 26 February 2021Sara Wade & Karla Monterrubio-Gomez (Edinburgh): On MCMC…
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All rights reserved Language
English Date Created
March 26th, 2021
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An automated programme has been used to generate the subtitles on this talk.
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ICMS Publisher
ICMS Licence Type
All rights reserved Language
English Date Created
February 8th, 2021
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We work through how to apply large-sample theory to A/B testing.
Course Code
INFR08030 Publisher
David Sterratt Licence Type
Creative Commons - Attribution Non Commercial Language
English Date Created
January 28th, 2021
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We'll consider two aspects of the interpretation of confidence intervals
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INFR08030 Publisher
David Sterratt Licence Type
Creative Commons - Attribution Non Commercial Language
English Date Created
January 15th, 2021
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We introduce the bootstrap method of estimating confidence intervals
Course Code
INFR08030 Publisher
David Sterratt Licence Type
Creative Commons - Attribution Non Commercial Language
English Date Created
January 15th, 2021
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We'll derive confidence intervals for the mean of large samples, when we can assume the sampling distribution of the mean is normal.
Course Code
INFR08030 Publisher
David Sterratt Licence Type
Creative Commons - Attribution Non Commercial Language
English Date Created
January 15th, 2021
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We give a formal definition of confidence intervals, and show how the random interval can be converted to a random variable
Course Code
INFR08030 Publisher
David Sterratt Licence Type
Creative Commons - Attribution Non Commercial Language
English Date Created
January 15th, 2021
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We introduce the standard error of an estimator and, in particular, the standard error of the mean.
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INFR08030 Publisher
David Sterratt Licence Type
Creative Commons - Attribution Non Commercial Language
English Date Created
January 14th, 2021
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We'll introduce the concept of bias and variance of estimators.
Course Code
INFR08030 Publisher
David Sterratt Licence Type
Creative Commons - Attribution Non Commercial Language
English Date Created
January 14th, 2021
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We'll introduce the concept of estimating a population or distribution parameter from a sample.
Course Code
INFR08030 Publisher
David Sterratt Licence Type
Creative Commons - Attribution Non Commercial Language
English Date Created
January 14th, 2021
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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…
Course Code
PGEE11164 Licence Type
All rights reserved Date Created
November 12th, 2020
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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…
Course Code
PGEE11164 Licence Type
All rights reserved Date Created
October 28th, 2020
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This video introduces the maximum likelihood estimator (MLE) technique
as a way of determining a good estimator for a given probabilistic
problem. This method is very straightforward and intuitive,…
Course Code
PGEE11164 Licence Type
All rights reserved Date Created
October 26th, 2020
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In this video, the concepts in estimation theory introduced so far for
scalar random variables are extended to deal with estimating multiple
parameters, for example the mean and variance of a…
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PGEE11164 Licence Type
All rights reserved Date Created
October 25th, 2020
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In this video, the question of finding the lower bound on the performance of all estimators for a particular
probabilistic problem, as a benchmark with which to compare the
performance of a given…
Course Code
PGEE11164 Licence Type
All rights reserved Date Created
October 23rd, 2020
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This video introduces the simple mean square error (MSE) as a criterion
which trades-off bias and variance for an estimator. The relationship
between the MSE and bias and variance is defined. The…
Course Code
PGEE11164 Licence Type
All rights reserved Date Created
October 23rd, 2020
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