added some docstrings and the posterior class structure

GPy/inference/latent_function_inference/__init__.py

@@ -0,0 +1,24 @@
+__doc__ = """
+Inference over Gaussian process latent functions
+
+In all our GP models, the consistency propery means that we have a Gaussian
+prior over a finite set of points f. This prior is
+
+  math:: N(f | 0, K)
+
+where K is the kernel matrix.
+
+We also have a likelihood (see GPy.likelihoods) which defines how the data are
+related to the latent function: p(y | f).  If the likelihood is also a Gaussian, 
+the inference over f is tractable (see exact_gaussian_inference.py). 
+
+If the likelihood object is something other than Gaussian, then exact inference
+is not tractable. We then resort to a Laplace approximation (laplace.py) or
+expectation propagation (ep.py). 
+
+The inference methods return a "Posterior" instance, which is a simple
+structure which contains a summary of the posterior. The model classes can then
+use this posterior object for making predictions, optimizing hyper-parameters,
+etc.
+
+"""

GPy/inference/latent_function_inference/posterior.py

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+import numpy as np
+
+class Posterior(object):
+    """
+    An object to represent a Gaussian posterior over latent function values. 
+    """
+    def __init__(self, log_marginal, dL_dmean=None, cov=None, prec=None):
+        self._log_marginal = log_marginal
+
+        #TODO: accept the init arguments, make sure we've got enough information to compute everything.
+
+    @property
+    def mean(self):
+        if self._mean is None:
+            self._mean = ??
+        return self._mean
+
+    @property
+    def covariance(self):
+        if self._covariance is None:
+            self._covariance = ??
+        return self._covariance
+
+    @property
+    def precision(self):
+        if self._precision is None:
+            self._precision = ??
+        return self._precision
+
+    @prop
+
+
+
+