Bayesian Linear Regression - CEDAR

More documents

Recommendations

Info

Machine Learning ! ! ! ! !SrihariParametric <strong>Regression</strong> vs GP <strong>Regression</strong>!• In parametric regression we have several samples from whichwe learn the parameters!• In Gaussian processes we we view the samples as one hugeinput that has a Gaussian distribution !– with a mean and a covariance matrix!• A Gaussian process is a stochastic process X t , t ∈ T, forwhich any finite linear combination of samples has a jointGaussian Distribution!– any linear functional applied to the sample function X t will give a normallydistributed result. Notation-wise, one can write X ~ GP(m,K), meaningthe random function X is distributed as a GP with mean function m and35 covariance function K.!
Page 1 and 2: Machine Learning ! ! ! ! !SrihariBa
Page 3: Machine Learning ! ! ! ! !SrihariSh
Page 6 and 7: Machine Learning ! ! ! ! !SrihariLi
Page 8 and 9: Machine Learning ! ! ! ! !SrihariPr
Page 10 and 11: Machine Learning ! ! ! ! !SrihariSa
Page 12 and 13: Machine Learning ! ! ! ! !SrihariBa
Page 14 and 15: Sequential Bayesian LearningMachine
Page 20 and 21: Machine Learning ! ! ! ! !SrihariEx
Page 24 and 25: Machine Learning ! ! ! ! !SrihariEq
Page 30 and 31: Machine Learning ! ! ! ! !SrihariKe
Page 32 and 33: Machine Learning ! ! ! ! !SrihariPr
Page 36 and 37: Machine Learning ! ! ! ! !SrihariGa
Page 38 and 39: Machine Learning ! ! ! ! !SrihariSp
Page 40 and 41: Machine Learning ! ! ! ! !SrihariSa
Page 42 and 43: Machine Learning ! ! ! ! !SrihariGP
Page 44 and 45: Machine Learning ! ! ! ! !SrihariRe

Bayesian Linear Regression - CEDAR

Create successful ePaper yourself

Delete template?

Save as template?