Search for tag: "estimator"

18 Media

56:44 Sara Wade & Karla Monterrubio-Gomez… Sara Wade & Karla Monterrubio-Gomez (Edinburgh): On MCMC for variationally sparse Gaussian processes: A pseudo-marginal approach.	Sara Wade & Karla Monterrubio-Gomez (Edinburgh): On MCMC for variationally sparse Gaussian processes: A pseudo-marginal approach. 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… statistics research function likelihood estimator scheme points covariance parameters process approximation hyperparameters matrix poisson block point quadrature log locations framework approach pseudo From OLLIE Quinn on March 1st, 2021 0 likes 3 plays 0 Licence Type All rights reserved Language English Date Created March 26th, 2021
47:55 Michael Feischl 8 February 2021 Black box… Michael Feischl 8 February 2021 Black box algorithms for adaptive FEM	Michael Feischl 8 February 2021 Black box algorithms for adaptive FEM An automated programme has been used to generate the subtitles on this talk. icms owna error problem network algorithm approximation optimality mesh element elements rate refinement sequence estimator networks norm problems step solution space orthogonality From OLLIE Quinn on February 8th, 2021 0 likes 49 plays 0 Course Code ICMS Publisher ICMS Licence Type All rights reserved Language English Date Created February 8th, 2021
10:44 FDS-S2-03-1-3 Large sample theory of A/B testing FDS-S2-03-1-3 Large sample theory of A/B testing	FDS-S2-03-1-3 Large sample theory of A/B testing We work through how to apply large-sample theory to A/B testing. inf2-fds hat variance error minus standard estimator confidence expression b. interval estimate difference equation things parameter proportions sense alpha numbers population From David Sterratt on January 28th, 2021 0 likes 122 plays 0 Course Code INFR08030 Publisher David Sterratt Licence Type Creative Commons - Attribution Non Commercial Language English Date Created January 28th, 2021
07:06 FDS-S2-01-2-8 Interpretation of confidence… FDS-S2-01-2-8 Interpretation of confidence intervals	FDS-S2-01-2-8 Interpretation of confidence intervals We'll consider two aspects of the interpretation of confidence intervals inf2-fds squirrels confidence interval ball n. sample machine distribution food estimator errors proportion sort bearing estimate millimetres minus jet bearings intervals From David Sterratt on January 15th, 2021 0 likes 119 plays 0 Course Code INFR08030 Publisher David Sterratt Licence Type Creative Commons - Attribution Non Commercial Language English Date Created January 15th, 2021
13:08 FDS-S2-01-2-7 Bootstrap confidence intervals FDS-S2-01-2-7 Bootstrap confidence intervals	FDS-S2-01-2-7 Bootstrap confidence intervals We introduce the bootstrap method of estimating confidence intervals inf2-fds sample bootstrap distribution confidence interval regression population variance estimate star bootstrapping table replacement loop things estimator idea coefficients coefficient intervals From David Sterratt on January 15th, 2021 0 likes 134 plays 0 Course Code INFR08030 Publisher David Sterratt Licence Type Creative Commons - Attribution Non Commercial Language English Date Created January 15th, 2021
12:22 FDS-S2-01-2-6 Estimating the confidence interval… FDS-S2-01-2-6 Estimating the confidence interval of means of large samples	FDS-S2-01-2-6 Estimating the confidence interval of means of large samples We'll derive confidence intervals for the mean of large samples, when we can assume the sampling distribution of the mean is normal. inf2-fds distribution confidence sample interval error standard method population intervals minus reservation estimator alpha times assumptions papers equals m. root n. From David Sterratt on January 15th, 2021 0 likes 139 plays 0 Course Code INFR08030 Publisher David Sterratt Licence Type Creative Commons - Attribution Non Commercial Language English Date Created January 15th, 2021
08:57 FDS-S2-01-2-5 Definition of confidence intervals FDS-S2-01-2-5 Definition of confidence intervals	FDS-S2-01-2-5 Definition of confidence intervals We give a formal definition of confidence intervals, and show how the random interval can be converted to a random variable inf2-fds interval estimator theta error variable standard times definition confidence probability hat statement alpha minus cases variables parameter sample formulation quantity From David Sterratt on January 15th, 2021 0 likes 138 plays 0 Course Code INFR08030 Publisher David Sterratt Licence Type Creative Commons - Attribution Non Commercial Language English Date Created January 15th, 2021
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 162 plays 0 Course Code INFR08030 Publisher David Sterratt Licence Type Creative Commons - Attribution Non Commercial Language English Date Created January 14th, 2021
11:34 FDS-S2-01-2-2 Bias and variance FDS-S2-01-2-2 Bias and variance	FDS-S2-01-2-2 Bias and variance We'll introduce the concept of bias and variance of estimators. inf2-fds estimator variance distribution bias sample expectation estimate parameter theta error bar hat bit estimators variable mu samples difference minus sense From David Sterratt on January 14th, 2021 0 likes 173 plays 0 Course Code INFR08030 Publisher David Sterratt Licence Type Creative Commons - Attribution Non Commercial Language English Date Created January 14th, 2021
09:48 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 195 plays 0 Course Code INFR08030 Publisher David Sterratt Licence Type Creative Commons - Attribution Non Commercial Language English Date Created January 14th, 2021
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 on November 14th, 2020 0 likes 77 plays 0 Course Code PGEE11164 Licence Type All rights reserved Date Created November 12th, 2020
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
15:28 Topic 48: Maximum Likelihood Estimation (PETARS,… Topic 48: Maximum Likelihood Estimation (PETARS, Chapter 6)	Topic 48: Maximum Likelihood Estimation (PETARS, Chapter 6) 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,… likelihood estimator function variance observations estimation alpha feta parameters method optimization problem fact boundary gradient estimators theta technique parameter theory From James Hopgood on October 26th, 2020 0 likes 76 plays 0 Course Code PGEE11164 Licence Type All rights reserved Date Created October 26th, 2020
24:39 Topic 47: Cramer-Rao Lower Bound for Parameter… Topic 47: Cramer-Rao Lower Bound for Parameter Vectors (PETARS, Chapter 6)	Topic 47: Cramer-Rao Lower Bound for Parameter Vectors (PETARS, Chapter 6) 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… pgee11164 matrix variance vector estimator parameters likelihood log function respect hat kramer covariance values form problem times parameter element expressions result From James Hopgood on October 26th, 2020 0 likes 77 plays 0 Course Code PGEE11164 Licence Type All rights reserved Date Created October 25th, 2020
23:17 Topic 46: Cramer-Rao Lower Bound (PETARS, Chapter… Topic 46: Cramer-Rao Lower Bound (PETARS, Chapter 6)	Topic 46: Cramer-Rao Lower Bound (PETARS, Chapter 6) 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… pgee11164 estimator variance function likelihood expectation kramer times log term estimators a. expression derivative summation respect form sigma variables parameter minus From James Hopgood on October 24th, 2020 0 likes 94 plays 0 Course Code PGEE11164 Licence Type All rights reserved Date Created October 23rd, 2020
14:51 Topic 44: Minimum Mean Square Error Estimators… Topic 44: Minimum Mean Square Error Estimators (PETARS, Chapter 6)	Topic 44: Minimum Mean Square Error Estimators (PETARS, Chapter 6) 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… pgee11164 error estimator variance bias sample expression ratio signal points sum times term a. parameter estimators function minimum values problem observations From James Hopgood on October 23rd, 2020 0 likes 75 plays 0 Course Code PGEE11164 Licence Type All rights reserved Date Created October 23rd, 2020

Search for tag: "estimator"

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