Repository: pints-team/pints

Issue:
Add kernel density estimation to pints.plot.histogram
KDE is not available in the `pints.plot.histogram` function.

Some code which manually adds KDE to a histogram was previously written for the [`plot-mcmc-pairwise-kde-plots`](https://github.com/pints-team/pints/blob/master/examples/plot-mcmc-pairwise-kde-plots.ipynb) example notebook:

```
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt

def plot_kde_1d(x, ax):
    """ Creates a 1d histogram and an estimate of the PDF using KDE. """
    xmin = np.min(x)
    xmax = np.max(x)
    x1 = np.linspace(xmin, xmax, 100)
    x2 = np.linspace(xmin, xmax, 50)
    kernel = stats.gaussian_kde(x)
    f = kernel(x1)
    hist = ax.hist(x, bins=x2, normed=True)
    ax.plot(x1, f)
```

Discussion:

Patch:
diff --git a/examples/README.md b/examples/README.md
index a46d45c4..69f67795 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -64,6 +64,7 @@ relevant code.
 - [Autocorrelation](./plotting/mcmc-autocorrelation.ipynb)
 - [Effective sample size](./sampling/effective-sample-size.ipynb)
 - [Evaluating noise models using autocorrelation plots of the residuals](./plotting/residuals-autocorrelation.ipynb)
+- [Histogram plots](./plotting/mcmc-histogram-plots.ipynb)
 - [Pairwise scatterplots](./plotting/mcmc-pairwise-scatterplots.ipynb)
 - [Predicted time series](./plotting/mcmc-predicted-time-series.ipynb)
 - [Trace plots](./plotting/mcmc-trace-plots.ipynb)
diff --git a/examples/plotting/mcmc-histogram-plots.ipynb b/examples/plotting/mcmc-histogram-plots.ipynb
new file mode 100644
index 00000000..e0283bca
--- /dev/null
+++ b/examples/plotting/mcmc-histogram-plots.ipynb
@@ -0,0 +1,196 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Inference plots - Histogram plots\n",
+    "\n",
+    "This example builds on [adaptive covariance MCMC](https://pints.readthedocs.io/en/latest/mcmc_samplers/adaptive_covariance_mc.html), and shows you how to plot the MCMC chain histograms, also known as the marginal posterior distributions.\n",
+    "\n",
+    "Other inference plots:\n",
+    "* [Predicted time series](mcmc-predicted-time-series.ipynb)\n",
+    "* [Autocorrelation](mcmc-autocorrelation.ipynb)\n",
+    "* [Pairwise scatterplots](mcmc-pairwise-scatterplots.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setting up an MCMC routine\n",
+    "\n",
+    "See the adaptive covariance MCMC example for details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "import pints\n",
+    "import pints.toy as toy\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Load a forward model\n",
+    "model = toy.LogisticModel()\n",
+    "\n",
+    "# Create some toy data\n",
+    "real_parameters = [0.015, 500]\n",
+    "times = np.linspace(0, 1000, 100)\n",
+    "org_values = model.simulate(real_parameters, times)\n",
+    "\n",
+    "# Add noise\n",
+    "noise = 50\n",
+    "values = org_values + np.random.normal(0, noise, org_values.shape)\n",
+    "real_parameters = np.array(real_parameters + [noise])\n",
+    "\n",
+    "# Get properties of the noise sample\n",
+    "noise_sample_mean = np.mean(values - org_values)\n",
+    "noise_sample_std = np.std(values - org_values)\n",
+    "\n",
+    "# Create an object with links to the model and time series\n",
+    "problem = pints.SingleOutputProblem(model, times, values)\n",
+    "\n",
+    "# Create a log-likelihood function (adds an extra parameter!)\n",
+    "log_likelihood = pints.GaussianLogLikelihood(problem)\n",
+    "\n",
+    "# Create a uniform prior over both the parameters and the new noise variable\n",
+    "log_prior = pints.UniformLogPrior(\n",
+    "    [0.01, 400, noise*0.1],\n",
+    "    [0.02, 600, noise*100]\n",
+    ")\n",
+    "\n",
+    "# Create a posterior log-likelihood (log(likelihood * prior))\n",
+    "log_posterior = pints.LogPosterior(log_likelihood, log_prior)\n",
+    "\n",
+    "# Perform sampling using MCMC, with three chains\n",
+    "xs = [\n",
+    "    real_parameters * 1.1,\n",
+    "    real_parameters * 1.15,\n",
+    "    real_parameters * 0.9,\n",
+    "]\n",
+    "mcmc = pints.MCMCController(log_posterior, 3, xs)\n",
+    "mcmc.set_max_iterations(6000)\n",
+    "mcmc.set_log_to_screen(False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Histograms\n",
+    "\n",
+    "The plots below show the histograms of the chains generated by three independent runs of the adaptive MCMC routine (all from the same starting point). All three chains require an initial period before they converge to the same parameter values. The initial period is usually discarded as 'burn-in'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running...\n",
+      "Done!\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Running...')\n",
+    "chains = mcmc.run()\n",
+    "print('Done!')\n",
+    "\n",
+    "# Discard 'burn-in'\n",
+    "chains = chains[:, 3000:, :]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simple histograms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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EnCvp9HT9W8AngDHp10HAT4GD0oT2XZIuxQDmSeqMiFfq+T4sf+avvmH98pduamEgZtZymzOK70CgM13/NPAQ8HS5D0TEf0oa1W3zJODQdPkq4D6SBDUJuDqdsXe2pCGSdkmPnRkRLwNImgkcCWxar8Msx8oNYDCzDepNUMOBsRHxFwBJZwO/i4gTenienSOiMCvvn4Cd0+VdgWVFx3Wl28pt34SkKSQV1xk5cmQPwzLrvaq9ZFzpBWWzZqq3Ft/OQPHMWm+yIbnUJW0tNaysRUTMiIiOiOjYcccdG3VaMzNrknpbUFcDD0m6JV3/DEkXXU/9WdIuEfFC2oW3It2+HBhRdNzwdNtyNnQJFrbfV8d1zara3HJGfZLnobIGqncU3/cl3Q4UCnN9JSIeruNUncCJwLnpv78u2n6qpOtJBkm8miaxO4D/K2n79LgjAP/UW9OLwZpZ9jbn5dqtgNURcYWkHSWNjohnyh0s6d9JWj/DJHWRjMY7F/ilpJOBZ9nwLtVtJEPMF5MMM/8KQES8LOl7wJz0uHMKAybMzKx3qXeYeWGo93uBK4AtgF+QzLJbUkSUq6+yyTTx6fOoU8qc53Lg8h6GbNartGwUYPdK56/UX1XdrJp6W1CfJZnSZj5ARDwvaduGRWVWgrvxzPqWehPUmxERkgJA0tYNjMn6qPHPzWCF3w9ar10T8qwlpefpmv1WUji41DxfZqXUm6B+KekSYIikrwInAT9vXFjWUpVGYoFHY9l6pWbdXdZvw+jHahMzmlVS7yi+H0qaCKwmeQ51VkTMbGhkZmbWp/U4QUnqD9yVFox1UjLrZVxpwvKixwkqIt6W9I6k7SLCQ3h6uVLPEwrPEsDPE/oiv8BszVLvM6g1wCNpsdbXChsj4usNicrMzPq8ehPUzemXmZlZJnqUoCSNjIjnPElg+7hg5lMV9ze8i67bCMDxz23cRTh75JTGXs/Meq2etqBuBcYCSLopIj7f+JCsXU1fMB26DTsuHnIMsFMzA7JcGf/cjGTh3qGlD/DrC9ZNT6fbUNHy7o0MxMzMrFhPW1BRZtn6kPV/CcPGfw2XeGnTzKxePU1QH5C0mqQlNShdJl2PiBjc0OisqaYvmJ4sFCWaRlcF2Ci5WZ+3USWKws9fkakHTG1iNJY3PUpQEdE/q0CsPRW/J9X9eZNZqarrD69aXvrgZx7Y5B2rWfOWABsG1/i9u76l3infzczMMrU5Exaa9VjL5jGytuYRgH2TE1QfV/ye1PzVSXfd8NWlu+qcXKwRMimVVKkCv5NX23KC6q3SX9juL8oW+IVZa0fl5poqmLB7iRaWp49pW05QZmapSpVXPECj+Zyg2sT0EkNwi3k4rpn1Nk5Q7eyZBzYsv+KZT6z36elzz1JD2KcO2b9R4ViTOUGZWZ9W3DtRGCjU3djBk5sVjhVxguqjSlV0WOFRemaWI35R18zMcsktqD7E7zGZWTtxguolppepJO76eNaXlHoJeNbLld+dmjXkrarnnb/6BqYvKFPFAo+izYoTVNb8kqCZWV2coMysTxu+el7ZfV2Dx61fnvXH8i2xdS8+5Rd5M+AEVQO/JGtmm8P/D6lPWyYoSUcCFwL9gUsj4twWh5SpC2Y+VfL9jEJR1xFDBjU7JLO2sTmDgyq1rmBDC6vaM6pGqJbkKqnU+gOYsMfQXCbJtktQkvoDFwMTgS5gjqTOiHisVTF96abvld03fPW8yjPRLvmnsrtc0NXM+rK2S1DAh4DFEbEEQNL1wCQguwRVXFKopH0yuWzxy7SVXqLNZPoCM6uquIW17OH6z3PGw3dutF787Ktgwh7ZttDySBHR6hh6RNIxwJER8bfp+peAgyLi1G7HTQEKTZD3Ak9WOO0w4KUMwm00x9lY7RBnO8QIjrPR2iHOemLcLSJ2rPXgdmxB1SQiZgCb1vMpQdLciOjIOKTN5jgbqx3ibIcYwXE2WjvE2YwY27HU0XJgRNH68HSbmZn1Iu2YoOYAYySNlvQu4Digs8UxmZlZg7VdF19EvCXpVOAOkmHml0fEos08bU1dgTngOBurHeJshxjBcTZaO8SZeYxtN0jCzMz6hnbs4jMzsz7ACcrMzHKp1yQoSUdKelLSYkmnl9i/paQb0v0PShqVbh8q6V5JayRdVObcnZIeLVrfQdJMSU+n/26fwxj/VdITkhZKukXSkFpibHacRdv/l6SQNCyvcUr6h/SeLpL0L3mMU9IBkmZLWiBprqQPtSpGSfel51yQfu1U6Vw5jPMbkh5Lf4fulrRbHuMs2v/59Heo5qHfzY5T0rHpPV0k6bqqAUZE23+RDJb4I7A78C7gD8A+3Y6ZCvwsXT4OuCFd3hr4MPA14KIS5/4ccB3waNG2fwFOT5dPB87LYYxHAAPS5fNqibEVcabbR5AMenkWGJbHOIHDgLuALdP1nXIa553AJ9LlTwL3tSpG4D6go8T1Sp4rh3EeBmyVLv99XuNM920L/Ccwu9wxrY4TGAM8DGxf6+9Qb2lBrS9/FBFvAoXyR8UmAVelyzcCh0tSRLwWEb8H3uh+UknbAN8AplU411XAZ/IWY0TcGRGFmdhmk7wvVotm30uAC4BvAj0ZsdPsOP8eODci1gFExIqcxhnA4HR5O+D5VsVYQclz5S3OiLg3Il5PV1v+O1TF90j+EO3J55od51eBiyPiFajtd6i3JKhdgWVF613ptpLHpP/jfhWoVtzqe8CPgNe7bd85Il5Il/8E7JzDGIudBNxeQ4xNj1PSJGB5RPyhxvhaEiewF3BI2s1xv6QDcxrnPwL/KmkZ8EOglhkxs4oR4Iq0m+c7RUmo3nM1O85iJ9P636GScUoaC4yIiN/VGF9L4iT5HdpL0n8p6YY+stpJekuCajhJBwB7RMQtlY6LpK3akrH6tcQo6Z+Bt4BrmxbYpjGUjFPSVsC3gbNaElg3Ve7nAGAHYDzwv4Ff1vhXf8NVifPvgdMiYgRwGnBZU4Pb2PERsR9wSPr1pRbGUknFOCWdAHQA/9qC2IptEqekfsD5wP9qaWQbK3c/B5B08x0KfBH4uao8G+8tCaqW8kfrj5E0gKT7o9IkKROADklLgd+TZP770n1/lrRLeq5dgFq6e5odI5K+DBxF8gNTaxJtZpx7AKOBP6T7hgPzJb07Z3FC8tflzZF4CHiHpFhm3uI8Ebg5Xf4VSTdOK2IkIpan//6F5FlZIZYen6tFcSLp48A/A0cXundzFue2wL7AfenPw3igs8aBEs2+n11AZ0T8NSKeAZ4iSVhl9ZYEVUv5o06SX16AY4B7Kv1POyJ+GhHviYhRJA8Dn4qIQ0uc60Tg13mLMW0+f5PkF6tS91/L4oyIRyJip4gYle7rAsZGxJ/yFGe6+1aSh+ZI2ovkoXItlZybHefzwEfT5Y8BT7ciRkkDlI7IlLQFyR9KhdGGPTpXq+KU9EHgEpLfoVqfOTY1zoh4NSKGFf0OzU7jnZunONPdt5K0nkiP2QtYUjHCqGG0Rzt8kYxYeopkVMo/p9vOSf9jAQwk+YtyMfAQsHvRZ5cCLwNrSP4n2X0kyyg2Hik1FLib5Jf/LmCHHMa4mKTveEH69bM83stu+5ZS4yi+FtzPdwG/IPllmw98LKdxfhiYRzIi60FgXCtiJBnlNQ9YCCwinQG72rlyFuddwJ/Z8DvUmcc4u133PmocxdeC+ymS7sjHgEeA46rF51JHZmaWS72li8/MzHoZJygzM8slJygzM8slJygzM8slJygzM8slJyizHpD0dlrC5VFJv0qrYbScpG834BxfUFJl+p0aX/Q0y5QTlFnPrI2IAyJiX+BNkmrONZHUP7uw6HGCKhHPoyQV0v+zIRGZbSYnKLP6PQDsCSDpVknz0hbIlMIBSubL+ZGkPwATJJ0laU7aAptRVPDzPkkXKJnD6XFJB0q6WcmcY9OKzneCpIfSVtwlkvpLOhcYlG67ttxxpeIp/mYi4vGIeDLrm2ZWKycoszqkdck+QfJGPMBJETGOpKjo1yUVKj5vDTwYER+IZHqCiyLiwLQFNoikFEzBmxHRAfyMpHzWKSR11r6sZIK4vYHJwMERcQDwNkmdxdPZ0LI7vtxxZeIxy60BrQ7ArM0MkrQgXX6ADdXCvy7ps+nyCJIimCtJksNNRZ8/TNI3ga1IqqMvAn6T7ivUQXsEWBTplC6SlqTn/DAwDpiTNrwGUbpQ8eEVjusej1luOUGZ9czatFWynqRDgY8DEyLi9bSy+MB09xsR8XZ63EBgOkmttGWSzi46DqBQLfudouXC+gCSWmZXRUS1OZ4qHbc+HrO8cxef2ebbDnglTU7vI5nyoJRCMnpJyYy4x/TwOncDx0jaCUDSDpJ2S/f9Na0eXe04s7bhBGW2+f4DGCDpceBckikPNhERq4Cfk4yWu4NkuoOaRcRjwJnAnZIWAjOBXdLdM4CFkq6tclxZkj4rqYtk8MTvJN3Rk/jMGs3VzM3MLJfcgjIzs1xygjIzs1xygjIzs1xygjIzs1xygjIzs1xygjIzs1xygjIzs1xygjIzs1xygjIzs1xygjIzs1xygjIzs1xygjIzs1xygjIzs1xygjIzs1zqEzPqDhs2LEaNGtXqMMzM+rR58+a9FBE71np8n0hQo0aNYu7cua0Ow8ysT5P0bE+OdxefmZnlkhOUmZnlUqYJStKRkp6UtFjS6SX2bynphnT/g5JGpduPl7Sg6OsdSQek++5Lz1nYt1OW34OZmbVGZs+gJPUHLgYmAl3AHEmdEfFY0WEnA69ExJ6SjgPOAyZHxLXAtel59gNujYgFRZ87PiL8UMnaygUzn6q4/7SJezUpErP2kGUL6kPA4ohYEhFvAtcDk7odMwm4Kl2+EThckrod88X0s2Zm1odkmaB2BZYVrXel20oeExFvAa8CQ7sdMxn4927brki7975TIqEBIGmKpLmS5r744ov1fg9mZtYiuR4kIekg4PWIeLRo8/ERsR9wSPr1pVKfjYgZEdERER077ljzsHszM8uJLBPUcmBE0frwdFvJYyQNALYDVhbtP45uraeIWJ7++xfgOpKuRDMz62WyTFBzgDGSRkt6F0my6ex2TCdwYrp8DHBPRASApH7AsRQ9f5I0QNKwdHkL4CjgUczMrNfJbBRfRLwl6VTgDqA/cHlELJJ0DjA3IjqBy4BrJC0GXiZJYgUfAZZFxJKibVsCd6TJqT9wF/DzrL4HMzNrnUxLHUXEbcBt3badVbT8BvCFMp+9DxjfbdtrwLiGB2pmZrnTJ2rxmfUK9/6g8v7DzmhOHGZN4gRlfUeV/8FP3367svumHjC10dFsqloCMutjcj3M3MzM+i4nKDMzyyV38ZkVPPNA+X2vvNr0ZzzTVy0su2/qkP2bGIlZazhBmTVK1WdIn29KGGa9hbv4zMwsl5ygzMwsl9zFZ9aGSj6fWjAdaNKQeLMmcAvKzMxyyQnKzMxyqaYElU67bmZm1jS1tqCmS3pI0lRJ5evBmJmZNUhNgyQi4hBJY4CTgHmSHgKuiIiZmUZn1gamp4MTKDFwwS/UmtWv5mdQEfE0cCbwLeCjwE8kPSHpc1kFZ2ZmfVdNLShJ+wNfAT4FzAQ+HRHzJb0HmAXcnF2IZtYo61t7JXh4uuVNre9B/RtwKfDtiFhb2BgRz0s6M5PIzMysT6s1QX0KWBsRbwNI6gcMjIjXI+KazKIzM7M+q9ZnUHcBg4rWt0q3mZmZZaLWBDUwItYUVtLlrbIJyczMrPYuvtckjY2I+QCSxgFrq3wGSUcCFwL9gUsj4txu+7cErgbGASuByRGxVNIo4HHgyfTQ2RHxtaJrX0nSorsN+J8RETV+H2ab5YKZT22ybf7qlQBMaOO6LLP+uJJ1L276vRWcNnGvJkZjlqg1Qf0j8CtJzwMC3g1MrvQBSf2Bi4GJQBcwR1JnRDxWdNjJwCsRsaek44Dzis77x4g4oMSpfwp8FXiQJEEdCdxe4/dhZmZtotYXdedIeh/w3nTTkxHx1yof+xCwOCKWAEi6HpgEFCeoScDZ6fKNwEWSVO6EknYBBkfE7HT9auAzOEFZG5q/+oaN1qf3e6zMkWZ9U0+m2zgQGJV+ZqwkIuLqCsfvCiwrWu8CDip3TES8JelVYGi6b7Skh4HkW3qqAAAgAElEQVTVwJkR8UB6fFe3c+5a6uKSpgBTAEaOHFn1mzPL2vjnZmy0vqLf4o0PGDIIM9ug1hd1rwH2ABYAb6ebg+T5URZeAEZGxMr0mdOtkt7fkxNExAxgBkBHR4efUZmZtZlaW1AdwD49HIywHBhRtD483VbqmC5JA4DtgJXpddYBRMQ8SX8E9kqPH17lnNZb3fuDyvsPO6NipYRStfJ6i2Wr1tL1x2SwRqnBDh7kYO2o1nFHj5IMjOiJOcAYSaMlvQs4DujsdkwncGK6fAxwT0SEpB3TQRZI2h0YAyyJiBeA1ZLGp8+q/gfw6x7GZWZmbaDWFtQw4LG0ivm6wsaIOLrcB9JnSqcCd5AMM788IhZJOgeYGxGdwGXANZIWAy+TJDGAjwDnSPor8A7wtYh4Od03lQ3DzG/HAyQsJ5at2vTNi1kvr2xBJGa9Q60J6ux6Th4Rt5EMBS/edlbR8hvAF0p87ibgpjLnnAvsW0881vvN+mP5hDB89VpGeCCCWduodZj5/ZJ2A8ZExF2StiJpFZlZBZ3dR+q10r0/KPscbvjqtTC44quNZk1X6yi+r5IM2d6BZDTfrsDPgMOzC82s8Up1wxXryy2s7sPgi826DGaPnFJynwdgWFZq7eI7heTF2wchmbxQ0k6ZRWVmm6iWXFulVPmnYk5gVq9aE9S6iHizUOQhHRLud4vM2sQFM59i/HMrWdYvn0nOrJRaE9T9kr4NDJI0kWQk3W+yC8usNcq1Uma9vBKW/BPjS+zbpCJEi3UvoVSQtzjNqqk1QZ1OUtj1EeDvSEbmXZpVUGZ1ufcHDF89r9VRmFmD1DqK7x3g5+mXWZ/TjNF4eX3GVFCuZQYw1iMALQO1juJ7hhLPnCJi94ZHZGZmRs9q8RUMJHm5dofGh2Nm9XL3pvU2NdXii4iVRV/LI+LHwKcyjs3MzPqwWrv4xhat9iNpUfVkLikzM7MeqTXJ/Kho+S1gKXBsw6Mxq2LWkirFV2utz29muVfrKL7Dsg7EzMysWK1dfN+otD8izm9MOGZmZomejOI7kA0TDn4aeAh4OougrG8oN/ttuSkzhq+eB/3g6Hf2zDIsa7BKsxxPPWBqEyOxdlNrghoOjI2IvwBIOhv4XUSckFVgZmbWt9X6SHln4M2i9TfTbWZmZpmotQV1NfCQpFvS9c8AV2UTkpmZWe2j+L4v6XbgkHTTVyLi4ezCMjOzvq4nb41sBayOiAuBLkmjM4rJzMys5mHm3yUZyfde4ApgC+AXwMHZhWZWWjMqi1vPVKp0PmHHoRU/W21G3ko8W2/vVmsL6rPA0cBrABHxPLBttQ9JOlLSk5IWSzq9xP4tJd2Q7n9Q0qh0+0RJ8yQ9kv77saLP3Jeec0H65annzcx6oVoHSbwZESEpACRtXe0DkvoDFwMTgS5gjqTOiHis6LCTgVciYk9JxwHnAZOBl4BPR8TzkvYF7gB2Lfrc8RExt8bYLWN+z8XMslBrgvqlpEuAIZK+CpxE9ckLPwQsjoglAJKuByYBxQlqEnB2unwjcJEkdRuAsYhkqvktI2JdjfFajhW6dOavrlJXz9pK2ek+nhmU/Dv6kNL7zcqodRTfDyVNBFaTPIc6KyJmVvnYrsCyovUu4KByx0TEW5JeBYaStKAKPg/M75acrpD0NnATMC0iNplMUdIUYArAyJEjq4RqZtV4vilrtqoJKu2quystGFstKTWUpPeTdPsdUbT5+IhYLmlbkgT1JZL3tDYSETOAGQAdHR2bJDAzy5fxz82ouH/2yClNisTyomqCioi3Jb0jabuIeLUH514OjChaH55uK3VMl6QBwHbASgBJw4FbgP8REX8simd5+u9fJF1H0pW4SYKyHLr3BwCMfy7p2lvRbTRe1+BxTQ/JzPKr1mdQa4BHJM0kHckHEBFfr/CZOcCY9H2p5cBxwH/vdkwncCIwCzgGuCcdjDEE+B1wekT8V+HgNIkNiYiXJG0BHAXcVeP3YGYVePi+5U2tCerm9Ktm6TOlU0lG4PUHLo+IRZLOAeZGRCdwGXCNpMXAyyRJDOBUYE/gLElnpduOIEmOd6TJqT9Jcqo2WMPMzNpQxQQlaWREPBcRddXdi4jbgNu6bTuraPkN4AslPjcNmFbmtO4HaiMbDUFftRCAZf3Wtigaa4Vlq5L/3l0lplFZ92L9L+la71etBXUrMBZA0k0R8fnsQ7K8qfSek1mtSo0CHL+q/GPt7l2OXUXVKsYOntzzANJnoGUddkbPz2mZqpagVLS8e5aBWO9RfsJBt5zMrHbVElSUWba+6JkHgA1dNt15FJ6ZNVK1BPUBSatJWlKD0mXS9YiIwZlGZ2a9mkcOWiUVE1RE9G9WINb+XGnAzBqp1mHmZmYtVfwHUGFwxazLNuyvVmmi8IJ4sQm7V54KxFrLCaoPcdVxM2snTlCWG+4itFqVena12cPQLXd6MuW7mZlZ07gF1cv4pVoz6y2coPqo7i/Tdi85c9rEvZoZjpnZJpygDID5Rf33ANMXeHSTmbWWE5StVzxIYdnDLQzELEPFAyweXlU0Rd2C6R7NmjNOUL1UqXp4xQloeDODMTOrg0fxmZlZLrkFlWMXzCw/V44HMZhtrLiHYEWJd+o66/hz3L+DreUE1abK/eLMX116qgsz21RxZf6uP670BIo54wTVIrW8r1Qu2fgtebPmqzTStdLgimq/6x6YUZ4TlJkZSRdhpRl+V/Rb3KM5zwq9HOX+0Jywh1/lqMYJqo0U+thL9a+vP6ZZwZj1QRvVi3xm0IblV9LE5mnjGyrTBCXpSOBCoD9waUSc223/lsDVwDhgJTA5Ipam+84ATgbeBr4eEXfUcs62ls5YW+Ap0s2aqycTKBY/v5r1ctJKmv1W455heYBGhglKUn/gYmAi0AXMkdQZEY8VHXYy8EpE7CnpOOA8YLKkfYDjgPcD7wHuklT4L1LtnA3naSrMrBbjn5uxybYVRUmvJ12Elm0L6kPA4ohYAiDpemASUJxMJgFnp8s3AhdJUrr9+ohYBzwjaXF6Pmo4Z9PMqjLqZ5O/cu79wYblVQs3+gvMzHq/kl2Er5R+7lVqgsXCpIyVWlewGS2s4v9HldLkLswsE9SuwLKi9S7goHLHRMRbkl4FhqbbZ3f77K7pcrVzAiBpClCYYnONpCfr+B6q+gVnAQwDXuq+7xtZXLB9lbxHthHfo+pyeY825znDKVzfg6N/VO2AYcBL2f2/59ube4LdenJwrx0kEREzgE3b2xmQNDciOppxrXble1Sd71F1vkeV9bb7k2Wpo+XAiKL14em2ksdIGgBsRzJYotxnazmnmZn1AlkmqDnAGEmjJb2LZNBDZ7djOoET0+VjgHsiItLtx0naUtJoYAzwUI3nNDOzXiCzLr70mdKpwB0kQ8Ivj4hFks4B5kZEJ3AZcE06COJlkoRDetwvSQY/vAWcEhFvA5Q6Z1bfQw80pSuxzfkeVed7VJ3vUWW96v4oabCYmZnli6fbMDOzXHKCMjOzXHKC6gFJ/SU9LOm36frhkuZLWiDp95L2TLdvKekGSYslPShpVCvjbpYS9+dj6f15VNJV6UhNlPhJen8WShrb2sibQ9JSSY+kPy9z0207SJop6en03+3T7b5HG+7RFyQtkvSOpI5ux5+R3qMnJf231kTdXGXu0b9KeiL9WblF0pCi49v2HjlB9cz/BB4vWv8pcHxEHABcB5yZbl9fwgm4gKSEU1+w/v5I6gdcBRwXEfsCz7JhxOYnSEZmjiF5mfqnzQ+1ZQ6LiAOK3lU5Hbg7IsYAd6fr4HtUfI8eBT4H/GfxQd1Koh0JTE9LrPUF3e/RTGDfiNgfeAo4A9r/HjlB1UjScOBTwKVFmwMYnC5vBzyfLk8i+Z8zJCWcDk9LOPVaJe7PUODNiCjUZJkJfD5dngRcHYnZwBBJuzQ14Pwo/lm5CvhM0XbfIyAiHo+IUpVg1pdEi4hngOKSaH1KRNwZEW+lq7PZMLFBW98jJ6ja/Rj4JvBO0ba/BW6T1AV8iQ0VTzYq4QQUSjj1Zt3vz0vAgKIumWPY8JJ1qTJYu9L7BXCnpHlpKS6AnSPihXT5T8DO6bLv0YZ7VI7vUel7dBJwe7rc1vfICaoGko4CVkRE94mYTgM+GRHDgSuA85seXA6Uuj/pC9fHARdIegj4C8nUKX3ZhyNiLEn33SmSPlK8M71nff29j4r3yIAK90jSP5O8O3ptq4JrpF5bi6/BDgaOlvRJYCAwWNLvgPdFxIPpMTcA/5EuF0oydXUr4dRblbo/v4iIE4BDACQdARRKLPfJklURsTz9d4WkW0i6Wv4saZeIeCHtwluRHu57tOEe/WeZw32Piu6RpC8DRwGHx4YXXNv6HrkFVYOIOCMihkfEKJJWwT0kfbvbacM8VRPZMICiXAmnXqnU/YmIEyTtBOsnpvwW8LP0I53A/0hHqo0HXi3q5uqVJG0tadvCMnAEycP/4p+VE4Ffp8u+RxvuUTnlSqL1WuXukZKJXL8JHB0Rrxd9pK3vkVtQdUpLOX0VuEnSO8ArJH2/UKaEUx/0v9Puv37ATyPinnT7bcAnSR7Yvg58pUXxNdPOwC3pWJkBwHUR8R+S5gC/lHQyyUjHY9PjfY823KPPAv8G7Aj8TtKCiPhvlUqi9WLl7tFiYEtgZrpvdkR8rd3vkUsdmZlZLrmLz8zMcskJyszMcskJyszMcskJyszMcskJyszMcskJyqwHJL2dVpF+VNKvJG3V6pgAJH27AecoWxHbrBWcoMx6Zm1aRXpf4E3ga7V+MOMq0j1OUCXiKVkR26xVnKDM6vcAUJgD7Na0eOei4gKektZI+pGkPwATJJ0laU7aAptRqHIv6T5JF0iaK+lxSQdKulnJPFHTis53gqSH0lbcJUrm4DoXGJRuu7bccaXiKf5mKlTENmsJJyizOqQ1Fj8BPJJuOikixgEdwNclFarXbw08GBEfiIjfAxdFxIFpC2wQSe20gjfT+X1+RlLy6BRgX+DLkoZK2huYDByczkH2Nsl8ZKezoWV3fLnjysRTTnFFbLOWcKkjs54ZJGlBuvwASVkrSJLSZ9PlESQ1z1aSJIebij5/mKRvAlsBOwCLgN+k+zrTfx8BFhVq70lakp7zw8A4YE7a8BrEhuKyxQ6vcFz3eDbR2ypiW/tygjLrmbVpq2Q9SYcCHwcmRMTrku4jqeoO8Eah9pmkgcB0oCMilkk6u+g4gHXpv+8ULRfWBwACroqIas+GKh33RqVabGUqYpu1hLv4zDbfdsAraXJ6HzC+zHGFZPSSpG1IKt33xN3AMUVV4neQtFu676+StqjhuLIqVMQ2awknKLPN9x8kswc/TjKr8uxSB0XEKuDnJFNI3AHM6clFIuIx4EyS2VQXkoy6K0wDPwNYKOnaKsdVchGwLUlF7AWSflbtA2ZZcjVzMzPLJbegzMwsl5ygzMwsl5ygzMwsl5ygzMwsl5ygzMwsl5ygzMwsl5ygzMwsl5ygzMwsl5ygzMwsl5ygzMwsl5ygzMwsl5ygzMwsl5ygzMwsl3KVoCQdKelJSYslnV5i/zckPSZpoaS7a5njxszM2lNuEpSk/sDFwCeAfYAvStqn22EPk8xGuj9wI/AvzY3SzMyaJU9Tvn8IWBwRSwAkXQ9MAh4rHBAR9xYdPxs4oZYTDxs2LEaNGtW4SM3MrMfmzZv3UkTsWOvxeUpQuwLLita7gIMqHH8ycHu5nZKmAFMARo4cydy5cxsRo5mZ1UnSsz05PjddfD0h6QSgA/jXcsdExIyI6IiIjh13rDlhm5lZTuSpBbUcGFG0PjzdthFJHwf+GfhoRKxrUmxmZtZkeUpQc4AxkkaTJKbjgP9efICkDwKXAEdGxIrmh2i2sQtmPlVx/2kT92pSJGa9T266+CLiLeBU4A7gceCXEbFI0jmSjk4P+1dgG+BXkhZI6mxRuGZmlrE8taCIiNuA27ptO6to+eNND8rMzFoiNy0oMzOzYk5QZmaWS7nq4jPr8+79QeX9h53RnDjMcsAtKDMzyyUnKDMzyyUnKDMzyyUnKDMzyyUnKDMzyyUnKDMzyyUnKDMzyyUnKDMzyyW/qGvWbNVexjUzwC0oMzPLKScoMzPLJScoMzPLJScoMzPLpcwSlKT9sjq3mZn1flm2oKZLekjSVEnbZXgdMzPrhTJLUBFxCHA8MAKYJ+k6SROzup6ZmfUumT6DioingTOBbwEfBX4i6QlJn8vyumZm1v4ye1FX0v7AV4BPATOBT0fEfEnvAWYBN2d1bbNGuWDmU60OwazPyrKSxL8BlwLfjoi1hY0R8bykMzO8rpmZ9QJZJqhPAWsj4m0ASf2AgRHxekRck+F1zaqrVm7osDOaE0eDTV8wveT2qQdMbXIkZpsvy2dQdwGDita3SreZmZlVlWULamBErCmsRMQaSVtleD2zzMxffUPZfWMHT25iJGZ9R5YtqNckjS2sSBoHrK1wvJmZ2XpZtqD+EfiVpOcBAe8G/KemtYf0GdX451YCsKLf4o12dw0e1/SQzPqazBJURMyR9D7gvemmJyPir1ldz8zMepesJyw8EBiVXmesJCLi6oyvaWZmvUCWL+peA+wBLADeTjcH4ARlmfNwa7P2l2ULqgPYJyIiw2uY5dqXbvreJtuGr563fnnEkEGb7AeYOmT/zGIyaxdZjuJ7lGRghJmZWY9l2YIaBjwm6SFgXWFjRByd4TXNzKyXyDJBnZ3huc3KPmeq+3yrFm6ybVm/bF/dW7aq9Plnvbyy5PbZb21cvPa0iXvVdJ1K98rP5Syvshxmfr+k3YAxEXFXWkWif1bXMzOz3iXLUXxfBaYAO5CM5tsV+BlweFbXNGuFSmWQzKx+WQ6SOAU4GFgN6ycv3CnD65mZWS+S5TOodRHxpiQAJA0geQ/KrGlm/XHjZznrXkye4RRKGE3YfWjTYzKz2mSZoO6X9G1gkKSJwFTgN5U+IOlI4EKSZ1WXRsS53fZ/BPgxsD9wXETcmEnklh+V5m3afrvmxWFmTZdlgjodOBl4BPg74DaSGXZLktQfuBiYCHQBcyR1RsRjRYc9B3wZ+KeMYjbbbMUv4ppZ/bIcxfcO8PP0qxYfAhZHxBIASdcDk4D1CSoilqb73mlosGZmljtZjuJ7hhLPnCJi9zIf2RVYVrTeBRy0GdefQjKKkJEjR9Z7GjMza5Gsa/EVDAS+QDLkvCkiYgYwA6Cjo8ODM6xXumDmxi/uzl+98aCQCXt4EIi1r8yGmUfEyqKv5RHxY+BTFT6yHBhRtD483WZmZn1Qll18Y4tW+5G0qCpdbw4wRtJoksR0HPDfs4rPLM86u83gW9C1+gbGDq5zYupnHii9/ZVXk38PO6O+85plJMsuvh8VLb8FLAWOLXdwRLwl6VTgDpJh5pdHxCJJ5wBzI6JT0oHALcD2wKcl/Z+IeH9m34E1RcU6cU2MwzbVvQuxu1prAZrVI8tRfIfV8ZnbSIajF287q2h5DknXn/URs5aULpoKMGvIW37GYtaLZdnF941K+yPi/KyubWaNNf65GaV33Jv+geDuQctA1qP4DgQ60/VPAw8BT2d4TbMNnnmA4as3ns5i/KpXN1ovbqFlPbVGOyoUwl1R4pnY0e/s2exwrI/JMkENB8ZGxF8AJJ0N/C4iTsjwmtaHDF89D54pPWW6mbW/LKuZ7wy8WbT+ZrrNzMysqixbUFcDD0m6JV3/DHBVhtcza5pW1tvz/FPWV2Q5iu/7km4HDkk3fSUiHs7qembWc8tWrV0/vXz36eQbqlJVeg+wsDKybEEBbAWsjogrJO0oaXREPJPxNa0PWbbKAxsapdRIvVKDI8yaJcth5t8lGcn3XuAKYAvgFySz7JpZHap1LXYNHrfRevGEjd1HNJrlXZYtqM8CHwTmA0TE85K2zfB6lkOzLqs8ddeEk3/YpEisnHJllcxaLcsE9WZEhKQAkLR1htcysybr7LeYh1el9Zy7lauaeoCLVNnmyzJB/VLSJcAQSV8FTqL2yQutr7j3B7BqYdndnX551qzPynIU3w8lTQRWkzyHOisiZmZ1PTNrfxULB7tV1udkkqAk9QfuSgvGOimZmVmPZZKgIuJtSe9I2i4iXq3+CWtX1aZjGN+kOCzRyheIzRoty2dQa4BHJM0EXitsjIivZ3hNs4o8Ys2sfWSZoG5Ov8zMyiuuMlFqwMzoQzbdZn1CwxOUpJER8VxEuO5eH1F2riAzs82QRQvqVmAsgKSbIuLzGVzD2ky5rrXOpe5yM7PSskhQKlrePYPzm1nePPPAxuuv1DY2qnjCSNh00sgRQzzfV1+WRYKKMsvWy3kAgpk1UhYJ6gOSVpO0pAaly6TrERGDM7immbWJ6UUDIbq3mOo+Z5kXfP1yb3treIKKiP6NPqeZ5VO56U7OWPVgkyOx3ijLKd/NzMzqlvWEhdbLdO9Kmb96pSe1M7NMuAVlZma55BaUVVVcb2/+6pUVjjQzaxwnKKta8NXMrBWcoMysLVSaK8p6Jz+DMjOzXHILyqoqLgbrEXuWJ7P+uLLyHFjVXtQtrqTe3WFn1BeUNYwTlJnlW/c6f0WGr66/EsX0BdNLT+8BTB2yf93ntcZxgurFKvXZVyoBM3/1DRutu9VkZq3gBNULlBuFVxgSPmGPoRWrTY9/biWzR07JLD4zs3o4QfVR3Qt2dnVrNZnlQblaf7Wq9ArF/NUrmeBhYrnm/zxmZpZLbkG1icrvgHy8aXGY2QZZvuR+2sS9Mjt3u3CC6gOSobiNmXfHrJ10H/BTq+mrFsItX6x63IpuXZBHv7Pn+mU/1918TlC9QL2/hMUqvkti1qaq/Vwva1IcVp/cJShJRwIXAv2BSyPi3G77twSuBsYBK4HJEbG02XEWq9bMb0ZT3QnGrPU6i17J6D7waOzgyeuXi19+L2WzWl/py8ezlpQu7Fw4dzt0IeYqQUnqD1wMTAS6gDmSOiPisaLDTgZeiYg9JR0HnAdM3vRsjbO5/cwbVwPftLUzYY+hyUKFFxIBGH3IZsVhZs3T/Y/G8ateLXNkjlSqrAFNr66RqwQFfAhYHBFLACRdD0wCihPUJODsdPlG4CJJiohoZqDNtH6o7cN3ltw/vImxmFl9OnvywnvXN5l+yyCg8VUt1rfe7h3a0PNmIW8Jalc27hbuAg4qd0xEvCXpVWAo8FLxQZKmAIV28hpJT2YScW2G0S2+Yr9oYiBtpOI9s5J8z3ou9/fsFK5vdQhFvg2bd89268nBeUtQDRMRM4DKHb1NImluRHS0Oo524nvWc75nPed71nPNvGd5e1F3OTCiaH14uq3kMZIGANuRDJYwM7NeJG8Jag4wRtJoSe8CjgM6ux3TCZyYLh8D3NObnz+ZmfVVueriS58pnQrcQTLM/PKIWCTpHGBuRHQClwHXSFoMvEySxPIuF12Nbcb3rOd8z3rO96znmnbP5MaHmZnlUd66+MzMzAAnKDMzyyknqAaTNFDSQ5L+IGmRpP+Tbr9S0jOSFqRfB7Q61ryR1F/Sw5J+m66PlvSgpMWSbkgHzliqxP3yz1gVkpZKeiS9P3PTbTtIminp6fTf7VsdZ56UuWdnS1pe9LP2ySyu7QTVeOuAj0XEB4ADgCMljU/3/e+IOCD9WtC6EHPrfwKPF62fB1wQEXsCr5CUubINut8v8M9YLQ5L70/hXZ7TgbsjYgxwd7puG+t+zyD53Sz8rN2WxUWdoBosEmvS1S3SL49EqULScOBTwKXpuoCPkZSzArgK+Exrosuf7vfLNsskkp8v8M9ZrjhBZSDtelkArABmRsSD6a7vS1oo6YK0Krtt8GPgm8A76fpQYFVEvJWud5GUubJE9/tV4J+xygK4U9K8tBwawM4R8UK6/Cdg59aEllul7hnAqenP2uVZdYs6QWUgIt6OiANIKmF8SNK+wBnA+4ADgR2Ab7UwxFyRdBSwIiI8Z0gNKtwv/4xV9+GIGAt8AjhF0keKd6Yv/bvHY2Ol7tlPgT1IHmO8APwoiws7QWUoIlYB9wJHRsQLafffOuAKksrtljgYOFrSUuB6kq69C4EhaTkrKF32qq/a5H5J+oV/xqqLiOXpvyuAW0ju0Z8l7QKQ/ruidRHmT6l7FhF/Tv8Qfwf4ORn9rDlBNZikHSUNSZcHkcxt9UTRL4BI+rgfbV2U+RIRZ0TE8IgYRVIZ5J6IOJ4kuR+THnYi8OsWhZgrZe7XCf4Zq0zS1pK2LSwDR5Dco+Lyaf45K1LunhV+1lKfJaOftVyVOuoldgGuSidf7Af8MiJ+K+keSTsCAhYAX2tlkG3iW8D1kqYBD5OUubLyrvXPWEU7A7ck+ZsBwHUR8R+S5gC/lHQy8CxwbAtjzJty9+ya9DWGAJYCf5fFxV3qyMzMcsldfGZmlktOUGZmlktOUGZmlktOUGZmlktOUGZmlktOUGY9IOnttHrzo5J+JWmrVscEIOnbDTjH99LSNQsk3SnpPY2IzaxeHmZu1gOS1kTENunytcC8iDi/xs/2j4i3s46rB5/ZKB5JgyNidbr8dWCfiPC7VNYybkGZ1e8BYE8ASbemxTQXFRfUlLRG0o8k/QGYIOksSXPSFtiMtOoDku5LC7zOlfS4pAMl3ZzOUTSt6HwnKJlvbIGkS9LCxOcCg9Jt15Y7rlQ8xd9MITmltsY16azFnKDM6pDWCPwE8Ei66aSIGAd0AF+XNDTdvjXwYER8ICJ+D1wUEQdGxL7AIOCootO+mc638zOScjunAPsCX5Y0VNLewGTg4LQY8dvA8RFxOrA2nZfn+HLHlYmn+/f1fUnL0uPPasCtMqubE5RZzwxKp1KZCzzHhvJLX09bJbOBEcCYdPvbwE1Fnz9MySzBj5AUxX1/0b7O9N9HgEVp8dd1wJL0nIcD44A5aQyHA7uXiLHScd3j2UhE/HNEjACuBU6teCfMMuZafGY9szZtlawn6VDg48CEiHhd0n3AwF7Br+wAAAD2SURBVHT3G4XnPJIGAtOBjohYJunsouMgmY0Zkjme1hVtf4fkd1XAVRFxRpUYKx33Ro3Pwa4FbgO+W8OxZplwC8ps820HvJImp/cB48scV0hGL0nahg2V2mt1N3CMpJ0AJO0gabd0318lbVHDcWVJGlO0Ogl4oofxmTWUW1Bmm+8/gK9Jehx4kqSbbxMRsUrSz0mmJvgTMKcnF4mIxySdSTK7aT/gryTPqZ4FZgALJc1Pn0OVO66ScyW9l6TF9iyuhm4t5mHmZmaWS+7iMzOzXHKCMjOzXHKCMjOzXHKCMjOzXHKCMjOzXHKCMjOzXHKCMjOzXPr/METcdZ54wvMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pints.plot\n",
+    "pints.plot.histogram(chains)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Histograms with KDE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pints.plot.histogram([chains[0]], kde=True)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pints/plot.py b/pints/plot.py
index a8635873..a6f95b47 100644
--- a/pints/plot.py
+++ b/pints/plot.py
@@ -171,7 +171,7 @@ def function_between_points(f, point_1, point_2, padding=0.25, evaluations=20):
     return fig, axes
 
 
-def histogram(samples, ref_parameters=None, n_percentiles=None):
+def histogram(samples, kde=False, ref_parameters=None, n_percentiles=None):
     """
     Takes one or more markov chains or lists of samples as input and creates
     and returns a plot showing histograms for each chain or list of samples.
@@ -185,6 +185,9 @@ def histogram(samples, ref_parameters=None, n_percentiles=None):
         ``(n_lists, n_samples, n_parameters)``, where ``n_lists`` is the
         number of lists of samples, ``n_samples`` is the number of samples in
         one list and ``n_parameters`` is the number of parameters.
+    kde
+        Set to ``True`` to include kernel-density estimation for the
+        histograms.
     ref_parameters
         A set of parameters for reference in the plot. For example, if true
         values of parameters are known, they can be passed in for plotting.
@@ -192,8 +195,15 @@ def histogram(samples, ref_parameters=None, n_percentiles=None):
         Shows only the middle n-th percentiles of the distribution.
         Default shows all samples in ``samples``.
     """
+    import matplotlib
     import matplotlib.pyplot as plt
     import numpy as np
+    import scipy.stats as stats
+    from distutils.version import LooseVersion
+
+    # Check matplotlib version
+    use_old_matplotlib = LooseVersion(matplotlib.__version__) \
+        < LooseVersion("2.2")
 
     # If we switch to Python3 exclusively, bins and alpha can be keyword-only
     # arguments
@@ -237,9 +247,19 @@ def histogram(samples, ref_parameters=None, n_percentiles=None):
                 xmax = np.percentile(samples_j[:, i],
                                      50 + n_percentiles / 2.)
             xbins = np.linspace(xmin, xmax, bins)
-            axes[i, 0].hist(
-                samples_j[:, i], bins=xbins, alpha=alpha,
-                label='Samples ' + str(1 + j_list))
+            if use_old_matplotlib:  # pragma: no cover
+                axes[i, 0].hist(
+                    samples_j[:, i], bins=xbins, alpha=alpha, normed=True,
+                    label='Samples ' + str(1 + j_list))
+            else:
+                axes[i, 0].hist(
+                    samples_j[:, i], bins=xbins, alpha=alpha, density=True,
+                    label='Samples ' + str(1 + j_list))
+
+            # Add kde plot
+            if kde:
+                x = np.linspace(xmin, xmax, 100)
+                axes[i, 0].plot(x, stats.gaussian_kde(samples_j[:, i])(x))
 
         # Add reference parameters if given
         if ref_parameters is not None:


Test patch:
diff --git a/pints/tests/test_plot.py b/pints/tests/test_plot.py
index a91d1479..529c665a 100755
--- a/pints/tests/test_plot.py
+++ b/pints/tests/test_plot.py
@@ -254,6 +254,9 @@ class TestPlot(unittest.TestCase):
         # Test compatiblity with one chain only
         pints.plot.histogram([self.samples[0]])
 
+        # Check kde gives no error
+        pints.plot.histogram(few_samples, kde=True)
+
         # Check n_percentiles gives no error
         pints.plot.histogram(few_samples, n_percentiles=50)
 
@@ -270,7 +273,8 @@ class TestPlot(unittest.TestCase):
             ValueError,
             r'Length of \`ref\_parameters\` must be same as number of'
             r' parameters\.',
-            pints.plot.histogram, self.samples, [self.real_parameters[0]]
+            pints.plot.histogram, self.samples,
+            ref_parameters=[self.real_parameters[0]]
         )
 
         # Test it works with single parameter