Search for tag: "likelihood"

ASR Lecture 14

ASR Lecture 14: Sequence discriminative training

From  Peter Bell on March 25th, 2021 0 likes 1 plays 0  

Statistics in COVID-19: Simon Wood (School of Mathematics, University of Edinburgh)

Simon Wood (School of Mathematics, University of Edinburgh): Does the data know it should follow the science? When was R<1 and incidence in decline? The CfS Research Day gathered together experts…

From  Belle Taylor on March 1st, 2021 0 likes 6 plays 0  

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…

From  OLLIE Quinn on March 1st, 2021 0 likes 12 plays 0  

ASR Lecture 4

ASR lecture 4: Introduction to HMMs

From  Peter Bell on February 1st, 2021 0 likes 15 plays 0  

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…

From  David Sterratt on January 20th, 2021 0 likes 125 plays 0  

NLU+ Lecture 3: Conditional Language Modelling with n-grams (2019 version)

Machine Translation Model

From  Ruairi O'Hare on December 17th, 2020 0 likes 6 plays 0  

NLU+ Lecture 3: Conditional Language Modelling with n-grams (2019 version)

Conditional Language Models

From  Ruairi O'Hare on December 17th, 2020 0 likes 8 plays 0  

Ruth King (University of Edinburgh) Title: To integrated models ... and beyond …

Friday 4 December Ruth King (University of Edinburgh)Title: To integrated models ... and beyond …Abstract: Capture-recapture studies are often conducted on wildlife populations in order to…

From  OLLIE Quinn on December 7th, 2020 0 likes 18 plays 0  

USMR lecture 9 part 3

the generalized linear model

From  Martin Corley on November 15th, 2020 0 likes 164 plays 0  

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…

From  James Hopgood on October 28th, 2020 0 likes 63 plays 0  

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,…

From  James Hopgood on October 26th, 2020 0 likes 76 plays 0  

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…

From  James Hopgood on October 26th, 2020 0 likes 78 plays 0  

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…

From  James Hopgood on October 24th, 2020 0 likes 95 plays 0  

Andrew Zammit Mangion (University of Wollongong) Title: Statistical Machine Learning for Spatio-Temporal Forecasting

Andrew Zammit Mangion (University of Wollongong)Title: Statistical Machine Learning for Spatio-Temporal ForecastingAbstract: Conventional spatio-temporal statistical models are well-suited for…

From  OLLIE Quinn on September 17th, 2020 0 likes 33 plays 0  

26a

Stationary Processes and the Markov Assumption

From  Alexandra Lascarides on July 17th, 2020 0 likes 114 plays 0