diff --git a/docs/source/core_classes_and_methods.rst b/docs/source/core_classes_and_methods.rst
index b49a62f25..aa3aae6e3 100644
--- a/docs/source/core_classes_and_methods.rst
+++ b/docs/source/core_classes_and_methods.rst
@@ -4,8 +4,9 @@ Core classes and methods
 
 .. currentmodule:: pints
 
-Pints provides the :class:`SingleOutputProblem` and
-:class:`MultiOutputProblem` classes to formulate
+Pints provides the :class:`SingleOutputProblem`,
+:class:`MultiOutputProblem`, :class:`ProblemCollection`
+and :class:`SubProblem` classes to formulate
 inverse problems based on time series data and
 :class:`ForwardModel`.
 
@@ -14,7 +15,9 @@ Overview:
 - :class:`ForwardModel`
 - :class:`ForwardModelS1`
 - :class:`MultiOutputProblem`
+- :class:`ProblemCollection`
 - :class:`SingleOutputProblem`
+- :class:`SubProblem`
 - :class:`TunableMethod`
 - :func:`version`
 
@@ -36,7 +39,10 @@ Forward model with sensitivities
 Problems
 ********
 
-.. autoclass:: SingleOutputProblem
-
 .. autoclass:: MultiOutputProblem
 
+.. autoclass:: ProblemCollection
+
+.. autoclass:: SingleOutputProblem
+
+.. autoclass:: SubProblem
diff --git a/examples/README.md b/examples/README.md
index dcd063b8a..412b5d0f1 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -99,6 +99,7 @@ relevant code.
 - [Multiplicative Gaussian noise](./stats/multiplicative-gaussian-errors.ipynb)
 - [Pooling parameters](./stats/pooling.ipynb)
 - [Student-t noise model](./stats/student-t-sampling-error.ipynb)
+- [Wragged output model](./stats/multiple_wragged_outputs.ipynb)
 
 
 ## Toy problems
diff --git a/examples/stats/multiple_wragged_outputs.ipynb b/examples/stats/multiple_wragged_outputs.ipynb
new file mode 100644
index 000000000..7aa8923bd
--- /dev/null
+++ b/examples/stats/multiple_wragged_outputs.ipynb
@@ -0,0 +1,424 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multi-output model with wragged time measurements\n",
+    "\n",
+    "This notebook describes how to fit a model in PINTS which has multiple outputs with different measurement times for each. Additionally, we illustrate how this same framework allows different measurement models to be used for different outputs within the same forward model when doing inference.\n",
+    "\n",
+    "For this, we consider the [Goodwin-Oscillator](../toy/model-goodwin-oscillator.ipynb) model, which has three outputs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pints\n",
+    "import pints.toy as toy\n",
+    "import pints.plot\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pints.noise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We first create some simulated data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = pints.toy.GoodwinOscillatorModel()\n",
+    "real_parameters = model.suggested_parameters()\n",
+    "times = model.suggested_times()\n",
+    "values = model.simulate(real_parameters, times)\n",
+    "\n",
+    "# add noise\n",
+    "noise1 = 0.001\n",
+    "noise2 = 0.01\n",
+    "noise3 = 0.1\n",
+    "noisy_values = np.array(values, copy=True)\n",
+    "noisy_values[:, 0] += np.random.normal(0, noise1, len(times))\n",
+    "noisy_values[:, 1] += np.random.normal(0, noise2, len(times))\n",
+    "noisy_values[:, 2] += np.random.normal(0, noise3, len(times))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we assume that only the first half of time measurements are taken for the first two outputs; and only the second half for the third."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "times_12 = times[:100]\n",
+    "outputs_12 = noisy_values[:100, :2]\n",
+    "times_3 = times[100:]\n",
+    "outputs_3 = noisy_values[100:, 2]\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.subplot(3, 1, 1)\n",
+    "plt.plot(times_12, outputs_12[:, 0], 'b')\n",
+    "plt.subplot(3, 1, 2)\n",
+    "plt.plot(times_12, outputs_12[:, 1], 'g')\n",
+    "plt.subplot(3, 1, 3)\n",
+    "plt.plot(times_3, outputs_3, 'r')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now create a `ProblemCollection` that allows us to construct problems for each of the outputs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "collection = pints.ProblemCollection(model, times_12, outputs_12,\n",
+    "    times_3, outputs_3)\n",
+    "problem_0 = collection.subproblem(0)\n",
+    "problem_1 = collection.subproblem(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each problem has a method for solving the ODE system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# solve system\n",
+    "sol_12 = problem_0.evaluate(real_parameters)\n",
+    "\n",
+    "## this uses cached result from first solve under the hood\n",
+    "sol_3 = problem_1.evaluate(real_parameters)\n",
+    "\n",
+    "# overlay ODE solutions over noisy data\n",
+    "plt.figure()\n",
+    "plt.subplot(3, 1, 1)\n",
+    "plt.plot(times_12, outputs_12[:, 0], 'b')\n",
+    "plt.plot(times_12, sol_12[:, 0])\n",
+    "plt.subplot(3, 1, 2)\n",
+    "plt.plot(times_12, outputs_12[:, 1], 'g')\n",
+    "plt.plot(times_12, sol_12[:, 1])\n",
+    "plt.subplot(3, 1, 3)\n",
+    "plt.plot(times_3, outputs_3, 'r')\n",
+    "plt.plot(times_3, sol_3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inference\n",
+    "We now describe how we can perform inference for the overall system. Here, we assume different likelihoods for each of the two output chunks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "log_likelihood_0 = pints.GaussianKnownSigmaLogLikelihood(problem_0, [noise1, noise2])\n",
+    "log_likelihood_1 = pints.GaussianLogLikelihood(problem_1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This means that each of our log-likelihoods have a different number of parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log_likelihood_0.n_parameters()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log_likelihood_1.n_parameters()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then aggregate these into a single callable object that we can use for inference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class CombinedLogLikelihood(pints.LogPDF):\n",
+    "    def __init__(self, log_likelihood_0, log_likelihood_1):\n",
+    "        self._log_likelihood_0 = log_likelihood_0\n",
+    "        self._log_likelihood_1 = log_likelihood_1\n",
+    "\n",
+    "    def __call__(self, x):\n",
+    "        return log_likelihood_0(x[:5]) + log_likelihood_1(x)\n",
+    "\n",
+    "    def n_parameters(self):\n",
+    "        return 6\n",
+    "    \n",
+    "combined_log_likelihood = CombinedLogLikelihood(log_likelihood_0, log_likelihood_1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using this, we now perform maximum likelihood estimation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Maximising LogPDF\n",
+      "Using Covariance Matrix Adaptation Evolution Strategy (CMA-ES)\n",
+      "Running in sequential mode.\n",
+      "Population size: 9\n",
+      "Iter. Eval. Best      Time m:s\n",
+      "0     9     -72.07721   0:00.2\n",
+      "1     18     561.0911   0:00.2\n",
+      "2     27     561.0911   0:00.3\n",
+      "3     36     925.9462   0:00.4\n",
+      "20    189    962.1331   0:01.4\n",
+      "40    369    967.3481   0:02.7\n",
+      "60    549    968.1981   0:03.9\n",
+      "80    729    968.257    0:05.1\n",
+      "100   909    968.2647   0:06.4\n",
+      "120   1089   968.3049   0:07.7\n",
+      "140   1269   968.3356   0:09.0\n",
+      "160   1449   968.3361   0:10.2\n",
+      "180   1629   968.3361   0:11.5\n",
+      "200   1809   968.3361   0:12.8\n",
+      "220   1989   968.3361   0:14.0\n",
+      "240   2169   968.3361   0:15.3\n",
+      "260   2349   968.3361   0:16.6\n",
+      "280   2529   968.3361   0:17.8\n",
+      "300   2709   968.3361   0:19.1\n",
+      "320   2889   968.3361   0:20.4\n",
+      "340   3069   968.3361   0:21.6\n",
+      "360   3249   968.3361   0:22.9\n",
+      "380   3429   968.3361   0:24.2\n",
+      "400   3609   968.3361   0:25.4\n",
+      "420   3789   968.3361   0:26.7\n",
+      "440   3969   968.3361   0:28.0\n",
+      "460   4149   968.3361   0:29.2\n",
+      "480   4329   968.3361   0:30.5\n",
+      "486   4374   968.3361   0:30.8\n",
+      "Halting: No significant change for 200 iterations.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define some boundaries\n",
+    "boundaries = pints.RectangularBoundaries([-5, -5], [5, 5])\n",
+    "\n",
+    "# Pick an initial point at actual parameter values\n",
+    "x0 = real_parameters.tolist() + [noise3]\n",
+    "\n",
+    "# And run!\n",
+    "xbest, fbest = pints.optimise(combined_log_likelihood, x0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1.97637205, 4.11780789, 0.11608288, 0.08182563, 0.1016085 ,\n",
+       "       0.09407352])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xbest"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now overlaying the estimated ODE solutions on top of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# solve system\n",
+    "sol_12_a = problem_0.evaluate(xbest[:5])\n",
+    "\n",
+    "## this uses cached result from first solve under the hood\n",
+    "sol_3_a = problem_1.evaluate(xbest[:5])\n",
+    "\n",
+    "# overlay ODE solutions over noisy data\n",
+    "plt.figure()\n",
+    "plt.subplot(3, 1, 1)\n",
+    "plt.plot(times_12, outputs_12[:, 0], 'b')\n",
+    "plt.plot(times_12, sol_12[:, 0])\n",
+    "plt.plot(times_12, sol_12_a[:, 0])\n",
+    "plt.subplot(3, 1, 2)\n",
+    "plt.plot(times_12, outputs_12[:, 1], 'g')\n",
+    "plt.plot(times_12, sol_12[:, 1])\n",
+    "plt.plot(times_12, sol_12_a[:, 1])\n",
+    "plt.subplot(3, 1, 3)\n",
+    "plt.plot(times_3, outputs_3, 'r')\n",
+    "plt.plot(times_3, sol_3)\n",
+    "plt.plot(times_3, sol_3_a)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The estimated solutions are close to the true solutions."
+   ]
+  }
+ ],
+ "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": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pints/__init__.py b/pints/__init__.py
index df559710b..75ae71467 100644
--- a/pints/__init__.py
+++ b/pints/__init__.py
@@ -66,7 +66,12 @@ def version(formatted=False):
 #
 from ._core import ForwardModel, ForwardModelS1
 from ._core import TunableMethod
-from ._core import SingleOutputProblem, MultiOutputProblem
+from ._core import (
+    SingleOutputProblem,
+    MultiOutputProblem,
+    ProblemCollection,
+    SubProblem
+)
 
 #
 # Utility classes and methods
diff --git a/pints/_core.py b/pints/_core.py
index adba9b54a..51cd53ae2 100644
--- a/pints/_core.py
+++ b/pints/_core.py
@@ -321,6 +321,262 @@ def values(self):
         return self._values
 
 
+class ProblemCollection(object):
+    """
+    Represents an inference problem where a model is fit to a multi-valued time
+    series, such as when measured from a system with multiple outputs, where
+    the different time series are potentially measured at different time
+    intervals.
+
+    This class is also of use when different outputs are modelled with
+    different likelihoods or score functions.
+
+    Parameters
+    ----------
+    model
+        A model or model wrapper extending :class:`ForwardModel`.
+    args
+        Consecutive times, values lists for each output chunk. For example,
+        times_1, values_1, times_2, values_2: where times_1 = [1.2, 2.5, 3] and
+        values_1 = [2.3, 4.5, 4.5]; times_2 = [4, 5, 6, 7] and
+        values_2 = [[3.4, 1.1, 0.5, 0.6], [1.2, 3.3, 4.5, 5.5]].
+    """
+    def __init__(self, model, *args):
+        self._model = model
+        if len(args) < 2:
+            raise ValueError('Must supply at least one time series.')
+        if len(args) % 2 != 0:
+            raise ValueError(
+                'Must supply times and values for each time series.')
+        self._timeses = []
+        self._valueses = []
+        self._output_indices = []
+
+        k = 0
+        self._n_output_sets = len(args) // 2
+        for i in range(self._n_output_sets):
+            times = np.array(args[k])
+            times_shape = times.shape
+            if len(times_shape) != 1:
+                raise ValueError('Times must be one-dimensional.')
+            values = np.array(args[k + 1])
+            values_shape = values.shape
+            if values_shape[0] != times_shape[0]:
+                raise ValueError('Outputs must be of same length as times.')
+            self._timeses.append(times)
+            self._valueses.append(values)
+            if len(values_shape) > 1:
+                n_outputs = values_shape[1]
+            else:
+                n_outputs = 1
+            self._output_indices.extend([i] * n_outputs)
+            k += 2
+        self._times_all = np.sort(list(set(np.concatenate(self._timeses))))
+        self._output_indices = np.array(self._output_indices)
+
+        # vars to handle caching across multiple output chunks
+        self._cached_output = None
+        self._cached_sensitivities = None
+        self._cached_parameters = None
+
+    def _output_sorter(self, y, index):
+        """
+        Returns output(s) corresponding to a given index at times corresponding
+        to that output.
+        """
+        # lookup times in times array
+        times = self._timeses[index]
+        time_indices = [np.where(self._times_all == x)[0][0] for x in times]
+
+        # find relevant output indices
+        output_indices = np.where(self._output_indices == index)[0]
+
+        # pick rows then columns
+        if y.ndim == 1:
+            y = np.expand_dims(y, axis=1)
+        y_short = y[time_indices, :]
+        y_short = y_short[:, output_indices]
+        if y_short.shape[1] == 1:
+            y_short = y_short.reshape((len(self._timeses[index]),))
+        return y_short
+
+    def _output_and_sensitivity_sorter(self, y, dy, index):
+        """
+        Returns output(s) and sensitivities corresponding to a given index at
+        times corresponding to that output.
+        """
+        # lookup times in times array
+        times = self._timeses[index]
+        time_indices = [np.where(self._times_all == x)[0][0] for x in times]
+
+        # find relevant output indices
+        output_indices = np.where(self._output_indices == index)[0]
+
+        # pick rows then columns
+        if y.ndim == 1:
+            y = np.expand_dims(y, axis=1)
+        y_short = y[time_indices, :]
+        y_short = y_short[:, output_indices]
+        if y_short.shape[1] == 1:
+            y_short = y_short.reshape((len(self._timeses[index]),))
+
+        # sort sensitivities
+        # if only single problem output
+        if dy.ndim == 2:
+            dy_short = dy[time_indices, :]
+        # multi-output problem
+        else:
+            dy_short = dy[time_indices, :, :]
+            dy_short = dy_short[:, output_indices, :]
+            if len(output_indices) == 1:
+                dy_short = dy_short.reshape(
+                    len(time_indices), 1, dy_short.shape[2])
+        return y_short, dy_short
+
+    def _evaluate(self, parameters, index):
+        """ Evaluates model or returns cached result. """
+        parameters = pints.vector(parameters)
+        if not np.array_equal(self._cached_parameters, parameters):
+            y = np.asarray(self._model.simulate(parameters, self._times_all))
+            self._cached_output = y
+            self._cached_parameters = parameters
+        return self._output_sorter(self._cached_output, index)
+
+    def _evaluateS1(self, parameters, index):
+        """ Evaluates model with sensitivities or returns cached result. """
+        parameters = pints.vector(parameters)
+
+        # extra or here catches if evaluate has been called before evaluateS1
+        if (not np.array_equal(self._cached_parameters, parameters) or
+                self._cached_sensitivities is None):
+            y, dy = self._model.simulateS1(parameters, self._times_all)
+            self._cached_output = y
+            self._cached_sensitivities = dy
+            self._cached_parameters = parameters
+        return self._output_and_sensitivity_sorter(
+            self._cached_output, self._cached_sensitivities, index)
+
+    def model(self):
+        """ Returns forward model. """
+        return self._model
+
+    def subproblem(self, index):
+        """
+        Creates a `pints.SubProblem` corresponding to a particular output
+        index.
+        """
+        if index >= self._n_output_sets:
+            raise ValueError('Index must be less than number of output sets.')
+        return pints.SubProblem(self, index)
+
+    def timeses(self):
+        """ Returns list of times sequences: one for each output chunk. """
+        return self._timeses
+
+    def valueses(self):
+        """ Returns list of value chunks: one for each output chunk. """
+        return self._valueses
+
+
+class SubProblem(object):
+    """
+    Represents an inference problem for a subset of outputs from a multi-output
+    model. This is likely to be used either when the measurement times across
+    outputs are differ or when different outputs require different objective
+    functions (i.e. log-likelihoods or score functions).
+
+    Parameters
+    ----------
+    collection
+        An object of :class:`ProblemCollection`.
+    index
+        An integer index corresponding to the particular output chunk in the
+        collection.
+    """
+    def __init__(self, collection, index):
+
+        # Get items from collection
+        self._collection = collection
+        self._index = index
+        model = collection.model()
+        self._model = model
+        timeses = collection.timeses()
+        self._times = pints.vector(timeses[index])
+        values = collection.valueses()
+        values = values[index]
+
+        self._n_parameters = int(model.n_parameters())
+        self._n_times = len(self._times)
+
+        values = np.array(values)
+        values_shape = values.shape
+
+        # here don't check array sizes as this will be done in the
+        # problemcollection.subproblem method
+        if len(values_shape) == 1:
+            self._n_outputs = 1
+
+            # copy so that they can no longer be changed
+            self._values = pints.vector(values)
+
+        else:
+            self._n_outputs = values_shape[1]
+            self._values = pints.matrix2d(values)
+
+    def evaluate(self, parameters):
+        """
+        Runs a simulation using the given parameters, returning the simulated
+        values.
+        """
+        return self._collection._evaluate(parameters, self._index)
+
+    def evaluateS1(self, parameters):
+        """
+        Runs a simulation using the given parameters, returning the simulated
+        values.
+        """
+        return self._collection._evaluateS1(parameters, self._index)
+
+    def n_outputs(self):
+        """
+        Returns the number of outputs for this problem.
+        """
+        return self._n_outputs
+
+    def n_parameters(self):
+        """
+        Returns the dimension (the number of parameters) of this problem.
+        """
+        return self._n_parameters
+
+    def n_times(self):
+        """
+        Returns the number of sampling points, i.e. the length of the vectors
+        returned by :meth:`times()` and :meth:`values()`.
+        """
+        return self._n_times
+
+    def times(self):
+        """
+        Returns this problem's times.
+
+        The returned value is a read-only NumPy array of shape
+        ``(n_times, n_outputs)``, where ``n_times`` is the number of time
+        points and ``n_outputs`` is the number of outputs.
+        """
+        return self._times
+
+    def values(self):
+        """
+        Returns this problem's values.
+
+        The returned value is a read-only NumPy array of shape
+        ``(n_times, n_outputs)``, where ``n_times`` is the number of time
+        points and ``n_outputs`` is the number of outputs.
+        """
+        return self._values
+
+
 class TunableMethod(object):
 
     """
diff --git a/pints/tests/test_subproblem_and_problemcollection.py b/pints/tests/test_subproblem_and_problemcollection.py
new file mode 100644
index 000000000..3dd83b00e
--- /dev/null
+++ b/pints/tests/test_subproblem_and_problemcollection.py
@@ -0,0 +1,258 @@
+#!/usr/bin/env python3
+#
+# Tests SubProblem and ProblemCollection classes
+#
+# This file is part of PINTS (https://github.com/pints-team/pints/) which is
+# released under the BSD 3-clause license. See accompanying LICENSE.md for
+# copyright notice and full license details.
+#
+import pints
+import pints.toy
+import numpy as np
+import unittest
+
+
+class TestProblemCollection(unittest.TestCase):
+    """
+    Tests ProblemCollection methods.
+    """
+    @classmethod
+    def setUpClass(cls):
+        """ Prepare problem for tests. """
+
+        model = pints.toy.GoodwinOscillatorModel()
+        real_parameters = model.suggested_parameters()
+        times = model.suggested_times()
+        values = model.simulate(real_parameters, times)
+        cls.model = model
+        cls.real_parameters = real_parameters
+        cls.times = times
+
+        # add noise
+        noise1 = 0.001
+        noise2 = 0.01
+        noise3 = 0.1
+        noisy_values = np.array(values, copy=True)
+        noisy_values[:, 0] += np.random.normal(0, noise1, len(times))
+        noisy_values[:, 1] += np.random.normal(0, noise2, len(times))
+        noisy_values[:, 2] += np.random.normal(0, noise3, len(times))
+
+        cls.times_12 = times[:100]
+        cls.outputs_12 = noisy_values[:100, :2]
+        cls.times_3 = times[100:]
+        cls.outputs_3 = noisy_values[100:, 2]
+
+    def test_problem_collection_methods(self):
+        # Tests problem collection
+
+        collection = pints.ProblemCollection(
+            self.model, self.times_12, self.outputs_12, self.times_3,
+            self.outputs_3)
+
+        # check overall times
+        timeses = collection.timeses()
+        times_stack = [self.times_12, self.times_3]
+        k = 0
+        for times in timeses:
+            self.assertTrue(np.array_equal(times, times_stack[k]))
+            k += 1
+
+        # check overall values
+        valueses = collection.valueses()
+        values_stack = [self.outputs_12, self.outputs_3]
+        k = 0
+        for values in valueses:
+            self.assertTrue(np.array_equal(values, values_stack[k]))
+            k += 1
+
+        # test subproblem classes
+        problem_0 = collection.subproblem(0)
+        problem_1 = collection.subproblem(1)
+
+        self.assertTrue(isinstance(problem_0, pints.SubProblem))
+        self.assertTrue(isinstance(problem_1, pints.SubProblem))
+
+        # check model returned
+        model = collection.model()
+        self.assertTrue(isinstance(model, pints.ForwardModelS1))
+
+    def test_univariate_problem(self):
+        # checks that method works with univariate output problem
+
+        model = pints.toy.LogisticModel()
+        real_parameters = [0.015, 500]
+        times = np.linspace(0, 1000, 1000)
+        values = model.simulate(real_parameters, times)
+
+        noise = 10
+        values += np.random.normal(0, noise, values.shape)
+        real_parameters.append(noise)
+        real_parameters = np.array(real_parameters)
+
+        # check times and values returned
+        collection = pints.ProblemCollection(
+            model, times, values)
+        timeses = collection.timeses()
+        self.assertTrue(np.array_equal(timeses[0], times))
+        valueses = collection.valueses()
+        self.assertTrue(np.array_equal(valueses[0], values))
+
+        # check subproblem can be made
+        problem_0 = collection.subproblem(0)
+        self.assertTrue(isinstance(problem_0, pints.SubProblem))
+
+        # check model returned
+        model = collection.model()
+        self.assertTrue(isinstance(model, pints.ForwardModelS1))
+
+    def test_problem_collection_errors(self):
+        # Tests problem collection errors
+
+        # supplied no data?
+        self.assertRaisesRegex(
+            ValueError, 'Must supply at least one time series.',
+            pints.ProblemCollection, self.model)
+
+        # supplied only a times vector without data?
+        self.assertRaisesRegex(
+            ValueError, 'Must supply times and values for each time series.',
+            pints.ProblemCollection, self.model, self.times_12,
+            self.outputs_12, self.times_3)
+
+        # supplied a 2d time vector?
+        self.assertRaisesRegex(
+            ValueError, 'Times must be one-dimensional.',
+            pints.ProblemCollection, self.model, self.outputs_12,
+            self.outputs_12)
+
+        # supplied times that aren't same length as outputs?
+        self.assertRaisesRegex(
+            ValueError, 'Outputs must be of same length as times.',
+            pints.ProblemCollection, self.model, [1, 2, 3, 4, 5],
+            self.outputs_12)
+
+        # selected index exceeding number of output chunks?
+        collection = pints.ProblemCollection(
+            self.model, self.times_12, self.outputs_12, self.times_3,
+            self.outputs_3)
+
+        self.assertRaisesRegex(
+            ValueError, 'Index must be less than number of output sets.',
+            collection.subproblem, 2
+        )
+
+
+class TestSubProblem(unittest.TestCase):
+    """
+    Tests SubProblem methods.
+    """
+    @classmethod
+    def setUpClass(cls):
+        """ Prepare problem for tests. """
+
+        model = pints.toy.GoodwinOscillatorModel()
+        real_parameters = model.suggested_parameters()
+        times = model.suggested_times()
+        values = model.simulate(real_parameters, times)
+        cls.model = model
+        cls.real_parameters = real_parameters
+        cls.times = times
+        cls.values = values
+
+        cls.times_12 = times[:100]
+        cls.outputs_12 = values[:100, :2]
+        cls.times_3 = times[100:]
+        cls.outputs_3 = values[100:, 2]
+
+        collection = pints.ProblemCollection(
+            cls.model, cls.times_12, cls.outputs_12, cls.times_3,
+            cls.outputs_3)
+        cls.problem_0 = collection.subproblem(0)
+        cls.problem_1 = collection.subproblem(1)
+
+        # also solve using sensitivity methods as ODE solution (due to
+        # numerics) very slightly different
+        val_s, dy = model.simulateS1(real_parameters, times)
+
+        cls.outputs_12_s = val_s[:100, :2]
+        cls.outputs_3_s = val_s[100:, 2]
+
+        cls.dy_0 = dy[:100, :2, :]
+
+        # reshape output here otherwise loses a dimension
+        dy_1 = dy[100:, 2, :]
+        cls.dy_1 = dy_1.reshape(100, 1, 5)
+
+    def test_evaluate(self):
+        # Tests that chunked solution same as splitting overall into bits
+
+        sol_0 = self.problem_0.evaluate(self.real_parameters)
+        sol_1 = self.problem_1.evaluate(self.real_parameters)
+
+        self.assertTrue(np.array_equal(sol_0, self.outputs_12))
+        self.assertTrue(np.array_equal(sol_1, self.outputs_3))
+
+    def test_evaluateS1(self):
+        # Tests that chunked solution and sens same as splitting overall
+
+        sol_0, dy_0 = self.problem_0.evaluateS1(self.real_parameters)
+        sol_1, dy_1 = self.problem_1.evaluateS1(self.real_parameters)
+
+        self.assertTrue(np.array_equal(sol_0, self.outputs_12_s))
+        self.assertTrue(np.array_equal(sol_1, self.outputs_3_s))
+        self.assertTrue(np.array_equal(self.dy_0, dy_0))
+        self.assertTrue(np.array_equal(self.dy_1, dy_1))
+
+    def test_methods(self):
+        # Tests methods return appropriate values
+
+        # outputs correct?
+        self.assertEqual(self.problem_0.n_outputs(), 2)
+        self.assertEqual(self.problem_1.n_outputs(), 1)
+
+        # parameters correct?
+        self.assertEqual(self.problem_0.n_parameters(), 5)
+        self.assertEqual(self.problem_1.n_parameters(), 5)
+
+        # times correct?
+        self.assertTrue(np.array_equal(self.times_12, self.problem_0.times()))
+        self.assertTrue(np.array_equal(self.times_3, self.problem_1.times()))
+
+        # n_times correct?
+        self.assertEqual(len(self.times_12), self.problem_0.n_times())
+        self.assertEqual(len(self.times_3), self.problem_1.n_times())
+
+        # values correct?
+        self.assertTrue(np.array_equal(
+            self.outputs_12, self.problem_0.values()))
+        self.assertTrue(np.array_equal(
+            self.outputs_3, self.problem_1.values()))
+
+    def test_univariate_problem(self):
+        # Tests that subproblems work with a univariate output problem
+
+        model = pints.toy.LogisticModel()
+        real_parameters = [0.015, 500]
+        times = np.linspace(0, 1000, 1000)
+        values = model.simulate(real_parameters, times)
+
+        # check that same returned from subproblem as when making a
+        # single output problem
+        collection = pints.ProblemCollection(
+            model, times, values)
+        problem = pints.SingleOutputProblem(
+            model, times, values)
+        problem_0 = collection.subproblem(0)
+        sol_0 = problem_0.evaluate(real_parameters)
+        sol_1 = problem.evaluate(real_parameters)
+        self.assertTrue(np.array_equal(sol_0, sol_1))
+
+        # check same true for sensitivities
+        sol_0, dy_0 = problem_0.evaluateS1(real_parameters)
+        sol_1, dy_1 = problem.evaluateS1(real_parameters)
+        self.assertTrue(np.array_equal(sol_0, sol_1))
+        self.assertTrue(np.array_equal(dy_0, dy_1))
+
+
+if __name__ == '__main__':
+    unittest.main()