![]() ![]() ![]() Once you have the marginal likelihood and its derivatives you can use any out-of-the-box solver such as (stochastic) Gradient descent, or conjugate gradient descent (Caution: minimize negative log marginal likelihood). We first review the definition and properties of Gaussian distribution:Ī Gaussian random variable $X\sim \mathcal$ by maximizing the marginal likelihood $P(y \mid X, \theta)$.Ĭf. Properties of Multivariate Gaussian Distributions
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