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The marginal likelihood is the integral of the likelihood times the prior$$ p(y|X) = \int p(y| f, X) p(f | X) df $$The term marginal likelihood refers 阅读全文
posted @ 2020-02-09 17:14
eliker
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1. Gaussian processesGaussian processes are the extension of multivariate Gaussians to infinite-size collections real valued variables. In particular, 阅读全文
posted @ 2020-02-09 17:13
eliker
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1. Feature mapsTo fit a non-lineal function, a more expressive family of models need to be created. Considering fitting cubic functions $y=\theta_3x^3 阅读全文
posted @ 2020-02-09 17:11
eliker
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Let \(S={(x^{(i)}, y^{(i)})}_{i=1}^m\) be a training set of i.i.d. examples from unknow distribution. The standard probabilistic interpretation of lin 阅读全文
posted @ 2020-02-09 17:09
eliker
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A vector-valued random variable $x\in \mathbb{R}^n$ is said to have a multivariate gaussian ( or normal) distribution with mean $\mu\in \mathbb{R}^n$ 阅读全文
posted @ 2020-02-09 17:08
eliker
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