# FUNCTION

# Public

# GP-CONFIDENCES-AS-PLOT-DATA (GP INPUTS &KEY MEANS COVARIANCES (LEVELS-AND-OPTIONS '((0 title 'mean' with lines) (-1.96 title 'mean - 1.96 * stddev' with lines) (1.96 title 'mean + 1.96 * stddev' with lines))))

Return a list of MGL-GNUPLOT:DATA-MAPPINGs, one for each level in
LEVELS-AND-OPTIONS (a list of (LEVEL OPTIONS)). Each mapping contains
INPUTS in its first column, and MEANS + LEVEL*VARIANCES in the
second.

# GP-SAMPLES-AS-PLOT-DATA (GP INPUTS &KEY MEANS COVARIANCES OPTIONS)

Returns a matrix that contains INPUTS in its first column, and a
sample taken with SAMPLE-GP in its second.

# UPDATE-GP (GP INPUTS OUTPUTS &KEY MEANS COVARIANCES)

Update GP with the evidence embodied by INPUTS and the
corresponding OUTPUTS. Return a new POSTERIOR-GP. If MEANS are
COVARIANCES are given, then the call to GP-MEANS-AND-COVARIANCES is
not made.

# Undocumented

# ->GP (&REST ARGS)

# GP-COVARIANCES (GP X1 &OPTIONAL (X2 X1))

# GP-MEANS-AND-COVARIANCES (GP X1 &OPTIONAL (X2 X1))

# Private

# SAMPLE-GP (GP INPUTS &KEY MEANS COVARIANCES)

Return a sample from the multivariate normal distribution defined
by GP at INPUTS as a column vector.

# Undocumented

# EXTRACT-COVARIANCES (LUMP STRIPE N-ROWS N-COLS)

# EXTRACT-MEANS (LUMP STRIPE)

# FIND-GP-LUMP (BPN)

# GAUSSIAN-KERNEL (X1 X2 &KEY SIGNAL-VARIANCE (BIAS-VARIANCE 0) LENGTH-SCALE (ROUGHNESS 2))

# GP-DATA-MATRIX (INPUTS OUTPUTS)

# GP-DATA-MATRIX-FOR-LEVEL (INPUTS MEANS COVARIANCES LEVEL)

# MAKE-MATRIX-FROM-LUMP-STRIPE (LUMP N-ROWS N-COLS STRIPE)

# MAKE-VECTOR-FROM-LUMP-STRIPE (LUMP STRIPE)

# POSTERIOR-GP-MEANS-AND-COVARIANCES (GP X1 X2 &KEY COMPUTE-COVARIANCES-P)

# UPDATE-GP* (GP INPUTS OUTPUTS MEANS COVARIANCES)

# GENERIC-FUNCTION

# Public

# GP-MEANS (GP X)

Returns the vector of means for the vector of
inputs X. X is a vector of arbitrary objects.

# GP-MEANS-AND-COVARIANCES* (GP X1 X2)

Returns two values: the means and the covariances
as matrices.

# Private

# GP-COVARIANCES* (GP X1 X2)

Returns the matrix of covariances between X1 and
X2. X1 and X2 are vectors of arbitrary objects. Noise is assumed to be
included in the covariance function.

# SLOT-ACCESSOR

# Public

# Undocumented

# COVARIANCE-LUMP-NAME (OBJECT)

# MEAN-LUMP-NAME (OBJECT)

# PRIOR-GP (OBJECT)

# Private

# Undocumented

# CENTERED-EVIDENCE-OUTPUTS (OBJECT)

# COVARIANCE-FN (OBJECT)

# COVARIANCES (OBJECT)

# EVIDENCE-INPUTS (OBJECT)

# EVIDENCE-OUTPUTS (OBJECT)

# INVERTED-COVARIANCES (OBJECT)

# MEAN-FN (OBJECT)

# MEANS (OBJECT)

# POSTERIOR-GPS (OBJECT)

# SETFPOSTERIOR-GPS (NEW-VALUE OBJECT)

# SAMPLES (OBJECT)

# SETFSAMPLES (NEW-VALUE OBJECT)

# CLASS

# Public

# PRIOR-GP (OBJECT)

A GP whose mean and covariance are defined by two
lisp functions. Can be updated, but it's not trainable.