- class tinygp.gp.ConditionResult(log_probability: JAXArray, gp: GaussianProcess)[source]#
The result of conditioning a
This has two entries,
gp, that are described below.
- count(value, /)#
Return number of occurrences of value.
- gp: GaussianProcess#
GaussianProcessdescribing the conditional distribution
This will have a mean and covariance conditioned on the observed data, but it is otherwise a fully functional GP that can sample from or condition further (although that’s probably not going to be very efficient).
- index(value, start=0, stop=9223372036854775807, /)#
Return first index of value.
Raises ValueError if the value is not present.
- log_probability: tinygp.helpers.JAXArray#
The log probability of the conditioned model
In other words, this is the marginal likelihood for the kernel parameters, given the observed data, or the multivariate normal log probability evaluated at the given data.