Richard Turner

Advances in Neural Processes

Richard Turner

1.6 Types of inference problem and an outline of the course

Richard Turner

1.5.1 The Dutch Book Theorem

Richard Turner

5.5.1 Mixture of Gaussians Question

Richard Turner

1.3.1 A little more information about Cox's theorem

Richard Turner

5.4 The K-Means Algorithm Limitations

Richard Turner

1.2 The radioactive decay problem and ad hoc solutions

Richard Turner

1.5 Bayesian Decision Theory

Richard Turner

1.4 The radioactive decay problem improving over ad hoc solutions and visualising the solution

Richard Turner

1.1 What is an inference problem

Richard Turner

5.2 The K-Means Algorithm

Richard Turner

5.3 The K-Means Algorithm Theory

Richard Turner

5.1 An Introduction to Clustering

Richard Turner

5.6 Mixtures of Gaussians Parameter Learning

Richard Turner

5.5 The Probabilistic Approach to Clustering

Richard Turner

1.3 The radioactive decay problem a principled approach

Richard Turner

6.15 Maximum likelihood learning of HMMs

Richard Turner

6.14.1 HMMs and Dynamic Programming

Richard Turner

5.11 EM for MoG Applied to Data

Richard Turner

6.10 Detailed derivation of general filtering equations

Richard Turner

5.13 Summary of Clustering and EM

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6.4.1 Questions about AR Models

Richard Turner

6.4.2 An Application of AR Models

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6.9 Overview of the general Kalman filtering recursion

Richard Turner

6.14 Computing the likelihood of the parameters in HMMs

Richard Turner

5.10.1 Free-energies for iid models

Richard Turner

6.1 Introduction to Sequence Modelling

Richard Turner

5.9 The Expectation Maximisation Algorithm

Richard Turner

6.7 Continuous Hidden State HMMs - LGSSMs

Richard Turner

5.7 The KL Divergence

Richard Turner