Inference. As discussed in Section 2.1.1, we optimize the negative log marginal likelihood. For the implementation this means we need a likelihood, a prior and data to perform the inference. i⋅ Remember that the covariance function is evaluated at all pairs of rows X of X to create the covariance matrix Kij = k(Xi⋅, Xj⋅, θ). The log marginal likelihood of the inference can be written as (Eq. (2.1)) log p Y X, θ, σ ( | 2) = (( (( )N | + 2 |)– 1 ) (− 1 t( +
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Inference. As discussed in Section 2.1.1, we optimize the negative log marginal likelihood. For the implementation this means we need a likelihood, a prior and data to perform the inference.
i⋅ Remember that the covariance function is evaluated at all pairs of rows X of X to create the covariance matrix Kij = k(Xi⋅, Xj⋅, θ). The log marginal likelihood of the inference can be written as (Eq. (2.1)) log p Y X, θ, σ ( | 2) = (( (( )N | + 2 |)– |)− 1 ) ({− 1 t( +
Appears in 1 contract
Sources: Thesis Deposit Agreement