MultiOutputGaussian

class gpjax.likelihoods.MultiOutputGaussian(num_datapoints, num_outputs, obs_stddev=1.0)

Bases: Gaussian

Gaussian likelihood with per-output noise variance.

Parameters:

• num_datapoints (int) – Total number of observations (N, not N*P).

• num_outputs (int) – Number of output dimensions (P).

• obs_stddev (Any) – Per-output noise standard deviation. Scalar broadcasts to [P].

noise_vector(n)

Per-observation noise variance in output-major (Kronecker) order.

Returns sigma_p^2 with each output’s variance repeated N times, concatenated across outputs: [sigma_1^2…sigma_1^2, sigma_2^2…sigma_2^2, …].

Parameters:

n (int)

Return type:

Float[jaxlib._jax.Array, ‘NP’] | Float[ndarray, ‘NP’]

prepare_targets(y, mx)

Reshape multi-output targets to output-major long format.

Parameters:

• y (Float[jaxlib._jax.Array, 'N P'] | Float[ndarray, 'N P'])

• mx (Float[jaxlib._jax.Array, 'N 1'] | Float[ndarray, 'N 1'])

Return type:

tuple[Float[jaxlib._jax.Array, ‘NP 1’] | Float[ndarray, ‘NP 1’], Float[jaxlib._jax.Array, ‘NP 1’] | Float[ndarray, ‘NP 1’]]