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2.3.7 Student's t-Distribution

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Sina Tootoonian

We discuss Student's tdistribution, first in the context of the ttest, and then as the marginal distribution of Gaussian data when the precision has been integrated out. We show that the distribution can have heavy tails, and discuss how this can be beneficial by making our models robust to outliers.

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2.3.8 Periodic Variables The von Mises distribution Pattern Recognition and Machine Learning

2.3.8 Periodic Variables The von Mises distribution Pattern Recognition and Machine Learning

2.3.9 Mixtures of Gaussians Pattern Recognition and Machine Learning

2.3.9 Mixtures of Gaussians Pattern Recognition and Machine Learning

2.3.6 Bayesian Inference for the Gaussian The Mean

2.3.6 Bayesian Inference for the Gaussian The Mean

2.3 The Gaussian Distribution Pattern Recognition and Machine Learning

2.3 The Gaussian Distribution Pattern Recognition and Machine Learning

2.3.6 Bayesian Inference for the Gaussian The Variance

2.3.6 Bayesian Inference for the Gaussian The Variance

2.3.8 Periodic Variables The Wrapped Normal Distribution

2.3.8 Periodic Variables The Wrapped Normal Distribution

2.1.1 The Beta Distribution Pattern Recognition and Machine Learning

2.1.1 The Beta Distribution Pattern Recognition and Machine Learning

2.3.5 Sequential Estimation Pattern Recognition and Machine Learning

2.3.5 Sequential Estimation Pattern Recognition and Machine Learning

2.4 Exponential Family What it is, Why You Should Care Pattern Recognition and Machine Learning

2.4 Exponential Family What it is, Why You Should Care Pattern Recognition and Machine Learning

2.1 Binary Variables Pattern Recognition and Machine Learning

2.1 Binary Variables Pattern Recognition and Machine Learning

2.3.4 Maximum Likelihood for the Gaussian Pattern Recognition and Machine Learning

2.3.4 Maximum Likelihood for the Gaussian Pattern Recognition and Machine Learning

2.3.1 Conditional Gaussian Distributions Pattern Recognition and Machine Learning

2.3.1 Conditional Gaussian Distributions Pattern Recognition and Machine Learning

Chapter 2 Probability Distributions Preview

Chapter 2 Probability Distributions Preview

Machine Learning in Biotechnology CB206v 02b Maximum Likelihood Estimation

Machine Learning in Biotechnology CB206v 02b Maximum Likelihood Estimation

2.3.3 Bayes' Theorem for Gaussian Variables Pattern Recognition and Machine Learning

2.3.3 Bayes' Theorem for Gaussian Variables Pattern Recognition and Machine Learning

M1 | Probability distributions | CIV8530 Structural & System Reliability [English version]

M1 | Probability distributions | CIV8530 Structural & System Reliability [English version]

Differentiating functions of vectors and matrices

Differentiating functions of vectors and matrices

2.4.1 & 2 Exponential Family Maximum Likelihood, Sufficient Statistics, Conjugate Priors PRML

2.4.1 & 2 Exponential Family Maximum Likelihood, Sufficient Statistics, Conjugate Priors PRML

Machine Learning in Biotechnology CB206v 03 classifiers, MLE, and Gaussian mixture models

Machine Learning in Biotechnology CB206v 03 classifiers, MLE, and Gaussian mixture models

2.2 Multinomial Variables Pattern Recognition and Machine Learning

2.2 Multinomial Variables Pattern Recognition and Machine Learning

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