Merge branch 'sampler_test' into 'master'

Multivariate Gaussian example

See merge request lscsoft/bilby!376

examples/other_examples/multidimensional_gaussian.py

+#!/usr/bin/env python
+"""
+Testing the recovery of a multi-dimensional
+Gaussian with zero mean and unit variance
+"""
+from __future__ import division
+import bilby
+import numpy as np
+
+# A few simple setup steps
+label = "multidim_gaussian"
+outdir = "outdir"
+
+# Generating simulated data: generating n-dim Gaussian
+
+dim = 5
+mean = np.zeros(dim)
+cov = np.ones((dim, dim))
+data = np.random.multivariate_normal(mean, cov, 100)
+
+
+class MultidimGaussianLikelihood(bilby.Likelihood):
+    """
+        A multivariate Gaussian likelihood
+        with known analytic solution.
+
+        Parameters
+        ----------
+        data: array_like
+            The data to analyse
+        dim: int
+            The number of dimensions
+        """
+
+    def __init__(self, data, dim):
+        self.dim = dim
+        self.data = np.array(data)
+        self.N = len(data)
+        self.parameters = {}
+
+    def log_likelihood(self):
+        mu = np.array(
+            [self.parameters["mu_{0}".format(i)] for i in range(self.dim)]
+        )
+        sigma = np.array(
+            [self.parameters["sigma_{0}".format(i)] for i in range(self.dim)]
+        )
+        p = np.array([(self.data[n, :] - mu) / sigma for n in range(self.N)])
+        return np.sum(
+            -0.5 * (np.sum(p ** 2) + self.N * np.log(2 * np.pi * sigma ** 2))
+        )
+
+
+likelihood = MultidimGaussianLikelihood(data, dim)
+priors = bilby.core.prior.PriorDict()
+priors.update(
+    {
+        "mu_{0}".format(i): bilby.core.prior.Uniform(-5, 5, "mu")
+        for i in range(dim)
+    }
+)
+priors.update(
+    {
+        "sigma_{0}".format(i): bilby.core.prior.LogUniform(0.2, 5, "sigma")
+        for i in range(dim)
+    }
+)
+# And run sampler
+# The plot arg produces trace_plots useful for diagnostics
+result = bilby.run_sampler(
+    likelihood=likelihood,
+    priors=priors,
+    sampler="dynesty",
+    npoints=500,
+    walks=10,
+    outdir=outdir,
+    label=label,
+    plot=True,
+)
+result.plot_corner()