pymc.gp.Marginal#

class pymc.gp.Marginal(*, mean_func=<pymc.gp.mean.Zero object>, cov_func=<pymc.gp.cov.Constant object>)[source]#

Marginal Gaussian process.

The gp.Marginal class is an implementation of the sum of a GP prior and additive noise. It has marginal_likelihood, conditional and predict methods. This GP implementation can be used to implement regression on data that is normally distributed. For more information on the marginal_likelihood, conditional and predict methods, see their docstrings.

Parameters:

mean_funcMean, default Zero: The mean function.
cov_func2D array_like, or Covariance, default Constant: The covariance function.

Examples

# A one dimensional column vector of inputs.
X = np.linspace(0, 1, 10)[:, None]

with pm.Model() as model:
    # Specify the covariance function.
    cov_func = pm.gp.cov.ExpQuad(1, ls=0.1)

    # Specify the GP.  The default mean function is `Zero`.
    gp = pm.gp.Marginal(cov_func=cov_func)

    # Place a GP prior over the function f.
    sigma = pm.HalfCauchy("sigma", beta=3)
    y_ = gp.marginal_likelihood("y", X=X, y=y, sigma=sigma)

...

# After fitting or sampling, specify the distribution
# at new points with .conditional
Xnew = np.linspace(-1, 2, 50)[:, None]

with model:
    fcond = gp.conditional("fcond", Xnew=Xnew)

Methods

`Marginal.__init__`(*[, mean_func, cov_func])
`Marginal.conditional`(name, Xnew[, ...])	Returns the conditional distribution evaluated over new input locations Xnew.
`Marginal.marginal_likelihood`(name, X, y[, ...])	Returns the marginal likelihood distribution, given the input locations X and the data y.
`Marginal.predict`(Xnew[, point, diag, ...])	Return the mean vector and covariance matrix of the conditional distribution as numpy arrays, given a point, such as the MAP estimate or a sample from a trace.
`Marginal.prior`(name, X, args, *kwargs)

Attributes

`X`
`sigma`
`y`