Geostat

Latest version: v0.11.1

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0.8.0

Geostat's syntax is now more in line with mathematical notation. A model is written as:

from geostat import GP, Model
import geostat.kernel as krn

gp = GP(0, krn.TrendPrior(...) + krn.SquaredExponential(...) + krn.Noise(...))
model = Model(gp, parameters = p_init, ...)

If you want to include a trend, do:

from geostat import GP, Model, Trend
import geostat.kernel as krn

gp = GP(
Trend(feat, beta=['b0', 'b1']),
krn.TrendPrior(...) + krn.SquaredExponential(...) + krn.Noise(...))
model = Model(gp, parameters = p_init, ...)

A compound GP can be created with `Mix`:

from geostat import GP, Model, Mix
import geostat.kernel as krn

latent1 = GP(0, krn.SquaredExponential(...))
latent2 = GP(0, krn.SquaredExponential(...))
obs1 = GP(0, krn.Noise(...))
obs2 = GP(0, krn.Noise(...))
obs3 = GP(0, krn.Noise(...))
obs_matrix = [[0., 1.], [1., 0.], ['c1', 'c2]]
gp = Mix([latent1, latent2], obs_matrix) + Mix([obs1, obs2, obs3])
model = Model(gp, parameters = p_init, ...)

If `obs_matrix` is not given with `Mix`, the identity matrix is assumed and under the hood I do an optimization where the component matrices for `obs1`, `obs2`, and `obs3` don't have to be fully computed.

0.6.3

Interpolation with `Model.predict()` used to not work with `gp.Trend` due to a math bug. This has been fixed.

0.6.1

0.6.0

GP.mcmc()` now does Bayesian inference using MCMC with replica exchange.

Breaking changes:

* `covfunc.Trend` is now `covfunc.TrendPrior`
* `GP.__init__()` now takes a `trend` argument, where the trend is specified explicitly using `geostat.Trend`.
* `GP` no longer takes a `hyperparameters` argument. Instead, when calling `fit()`, specify `iters` and/or `reg` as optional arguments.

See [mcmc-inference.ipynb](https://github.com/whdc/geostat/blob/main/doc/mcmc-inference.ipynb) for usage details.

0.4.4

There was a serious bug in `covfunc.Delta` that prevented it from working correctly. It was acting almost exactly like a nugget.

0.4.3

Previous releases of 0.4.x had a bug that affected multiGPs (GPs with more than one category of observations).

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