O'Reilly logo

Machine Learning by Sergios Theodoridis

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

12.5 Latent Variables and the EM Algorithm

At the end of Section 12.3, it was pointed out that the evidence function associated with the regression task in Eq. (12.3), assuming that p(y|θ) and p(θ) are Gaussians of the form given in (12.39), is also Gaussian parameterized via a set of parameters, ξ, where for this case ξ=[ση2,σθ2]si154_e, and we can write p(y;ξ). Maximizing the evidence with respect to ξ becomes a typical ML task. However, in general, such closed-form expressions for the evidence function are not possible, and the integration in (12.14) is intractable. The main source of difficulty is the fact that our regression model is described ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required