Repository: pymc-devs/pymc
Pull request: Fix random sampling in Mixture
The output from sample_ppc does not equal to the shape of the observed sometimes, as the `distribution.shape` of ObservedRVs are `[]`. Initially, this PR adds the specific shape (i.e., shape of the data) to the ObservedRVs, so that generating random sample will have the correct shape, which makes working with random method easier (e.g., PR #2984). However, doing so turns out breaks a lot of examples when `theano.shared` observed is used, as updating the value via `set_values` change the shape.

Now this PR just fix the mixture random error (close #2954).

Problem statement:
sample_ppc does not respect the size of Mixture likelihoods
When running `sample_ppc` on models with a `Mixture` likelihood, the resulting posterior sample is always comprised of scalar values. That is, a likelihood of size N that is sampled with `sample_ppc` for M iterations returns a trace of length (M,) rather than (M, N) as expected.

Hints from issue discussion:
Confirmed that this is the case for all `Mixture`s. It makes it impossible to do PPC with covariates.
Could be related to #2909 (in particular, https://github.com/pymc-devs/pymc3/issues/2909#issuecomment-376999288)
Here is an example where this happens.

```python
    # Location parameter a function of covariates
    ψ = [pm.Deterministic('ψ{}'.format(i), μ[i] + tt.dot(pos_shared[nonzero_day], β) 
                          + γ*age_shared[nonzero_day] + α*bonus_shared[nonzero_day]) 
                        for i in range(MIXTURE_COMPONENTS)]
    
    # Scale parameter, one per mixture component
    s = pm.HalfNormal('s', 5, shape=MIXTURE_COMPONENTS)
    
    # The Gumbel distribution is log-Weibull
    components = [pm.Gumbel.dist(ψ[i], s[i]) for i in range(MIXTURE_COMPONENTS)]
    
    # Mixture component weights
    w = pm.Dirichlet('w', a=np.ones(MIXTURE_COMPONENTS))  

    # Mixture likelihood
    y_obs = pm.Mixture('y_obs', w=w, comp_dists=components, observed=y_std[nonzero_day])
```

If I set the data to new values (all `shared`) and run `sample_ppc`:

```python
with value_model:
    pred = pm.sample_ppc(trace, samples=1000, vars=[y_obs]
```

I get a trace of shape `(1000,)`, and not `(1000, len(y_std[nonzero_day])`
Could you please quickly try setting the shape of `y_obs` as `shape=len(y_std[nonzero_day])`?
```
with value_model:
    pred = pm.sample_ppc(trace, samples=1000, vars=[y_obs], size=577)
  0%|          | 0/1000 [00:00<?, ?it/s]
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
~/anaconda/lib/python3.6/site-packages/pymc3/distributions/mixture.py in _comp_samples(self, point, size, repeat)
    134         try:
--> 135             samples = self.comp_dists.random(point=point, size=size, repeat=repeat)
    136         except AttributeError:

AttributeError: 'list' object has no attribute 'random'

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
<ipython-input-125-7710708281a9> in <module>()
      1 with value_model:
----> 2     weibull_pred = pm.sample_ppc(trace, samples=1000, vars=[y_obs], size=577)

~/anaconda/lib/python3.6/site-packages/pymc3/sampling.py in sample_ppc(trace, samples, model, vars, size, random_seed, progressbar)
   1064             for var in vars:
   1065                 ppc[var.name].append(var.distribution.random(point=param,
-> 1066                                                              size=size))
   1067 
   1068     except KeyboardInterrupt:

~/anaconda/lib/python3.6/site-packages/pymc3/distributions/mixture.py in random(self, point, size, repeat)
    165                                      dist_shape=self.shape,
    166                                      size=size).squeeze()
--> 167         comp_samples = self._comp_samples(point=point, size=size, repeat=repeat)
    168 
    169         if comp_samples.ndim > 1:

~/anaconda/lib/python3.6/site-packages/pymc3/distributions/mixture.py in _comp_samples(self, point, size, repeat)
    136         except AttributeError:
    137             samples = np.column_stack([comp_dist.random(point=point, size=size, repeat=repeat)
--> 138                                        for comp_dist in self.comp_dists])
    139 
    140         return np.squeeze(samples)

~/anaconda/lib/python3.6/site-packages/pymc3/distributions/mixture.py in <listcomp>(.0)
    136         except AttributeError:
    137             samples = np.column_stack([comp_dist.random(point=point, size=size, repeat=repeat)
--> 138                                        for comp_dist in self.comp_dists])
    139 
    140         return np.squeeze(samples)

~/anaconda/lib/python3.6/site-packages/pymc3/distributions/continuous.py in random(self, point, size, repeat)
   2182         return generate_samples(stats.gumbel_r.rvs, loc=mu, scale=sd,
   2183                                 dist_shape=self.shape,
-> 2184                                 size=size)
   2185 
   2186     def logp(self, value):

~/anaconda/lib/python3.6/site-packages/pymc3/distributions/distribution.py in generate_samples(generator, *args, **kwargs)
    492                                         prefix_shape,
    493                                         *args, **kwargs)
--> 494     return reshape_sampled(samples, size, dist_shape)

~/anaconda/lib/python3.6/site-packages/pymc3/distributions/distribution.py in reshape_sampled(sampled, size, dist_shape)
    408 
    409     if np.size(sampled) == 1 or repeat_shape or dist_shape:
--> 410         return np.reshape(sampled, repeat_shape + dist_shape)
    411     else:
    412         return sampled

~/anaconda/lib/python3.6/site-packages/numpy/core/fromnumeric.py in reshape(a, newshape, order)
    255            [5, 6]])
    256     """
--> 257     return _wrapfunc(a, 'reshape', newshape, order=order)
    258 
    259 

~/anaconda/lib/python3.6/site-packages/numpy/core/fromnumeric.py in _wrapfunc(obj, method, *args, **kwds)
     50 def _wrapfunc(obj, method, *args, **kwds):
     51     try:
---> 52         return getattr(obj, method)(*args, **kwds)
     53 
     54     # An AttributeError occurs if the object does not have

ValueError: cannot reshape array of size 332929 into shape (577,)
```
Yet, if I don't set it, the size is one, strangely.
What i meant is `y_obs = pm.Mixture('y_obs', w=w, comp_dists=components, observed=y_std[nonzero_day], shape=np.shape(y_std[nonzero_day]))` I have the suspicion that the default shape in mixture is not set correctly.
```
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-106-c774bf42fc26> in <module>()
      1 with value_model:
----> 2     weibull_pred = pm.sample_ppc(trace, samples=1000, vars=[y_obs], size=np.shape(y_std_late))

~/Repos/pymc3/pymc3/sampling.py in sample_ppc(trace, samples, model, vars, size, random_seed, progressbar)
   1064             for var in vars:
   1065                 ppc[var.name].append(var.distribution.random(point=param,
-> 1066                                                              size=size))
   1067 
   1068     except KeyboardInterrupt:

~/Repos/pymc3/pymc3/distributions/mixture.py in random(self, point, size, repeat)
    164                                      broadcast_shape=w.shape[:-1] or (1,),
    165                                      dist_shape=self.shape,
--> 166                                      size=size).squeeze()
    167         comp_samples = self._comp_samples(point=point, size=size, repeat=repeat)
    168 

~/Repos/pymc3/pymc3/distributions/distribution.py in generate_samples(generator, *args, **kwargs)
    478     if broadcast_shape == (1,) and prefix_shape == ():
    479         if size is not None:
--> 480             samples = generator(size=size, *args, **kwargs)
    481         else:
    482             samples = generator(size=1, *args, **kwargs)

~/Repos/pymc3/pymc3/distributions/mixture.py in random_choice(*args, **kwargs)
    156                 return np.row_stack([np.random.choice(k, p=w_) for w_ in w])
    157             else:
--> 158                 return np.random.choice(k, p=w, *args, **kwargs)
    159 
    160         w = draw_values([self.w], point=point)[0]

mtrand.pyx in mtrand.RandomState.choice()

~/anaconda3/envs/dev/lib/python3.6/site-packages/numpy/core/fromnumeric.py in prod(a, axis, dtype, out, keepdims)
   2561             pass
   2562         else:
-> 2563             return prod(axis=axis, dtype=dtype, out=out, **kwargs)
   2564 
   2565     return _methods._prod(a, axis=axis, dtype=dtype,

TypeError: prod() got an unexpected keyword argument 'out'
```
There should be no way of returning a single sample per iteration, however, because this is a covariate model, not independent samples. There are N individuals with their own covariate models, so there should always be N generated at every iteration.

If a single distribution is used instead of a mixture, the returned shape is correct.
Do you have a reproducible example? And does it work with the changes I made to #2979 #2909

Commit message: Fix test, add kwarg to NormalMixture

Also updated dependent_density_regression notebook

Patch to review:
diff --git a/docs/source/notebooks/dependent_density_regression.ipynb b/docs/source/notebooks/dependent_density_regression.ipynb
index 071e08972d..402e8d768e 100644
--- a/docs/source/notebooks/dependent_density_regression.ipynb
+++ b/docs/source/notebooks/dependent_density_regression.ipynb
@@ -5,8 +5,6 @@
    "metadata": {},
    "source": [
     "# Dependent density regression\n",
-    "Author: [Austin Rochford](https://github.com/AustinRochford/)\n",
-    "\n",
     "In another [example](dp_mix.ipynb), we showed how to use Dirichlet processes to perform Bayesian nonparametric density estimation.  This example expands on the previous one, illustrating dependent density regression.\n",
     "\n",
     "Just as Dirichlet process mixtures can be thought of as infinite mixture models that select the number of active components as part of inference, dependent density regression can be thought of as infinite [mixtures of experts](https://en.wikipedia.org/wiki/Committee_machine) that select the active experts as part of inference.  Their flexibility and modularity make them powerful tools for performing nonparametric Bayesian Data analysis."
@@ -15,49 +13,53 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on PyMC3 v3.4.1\n"
+     ]
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
-    "from IPython.display import HTML"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from matplotlib import animation as ani, pyplot as plt\n",
+    "import pymc3 as pm\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "import pymc3 as pm\n",
+    "from matplotlib import animation as ani, pyplot as plt\n",
     "import seaborn as sns\n",
-    "from theano import shared, tensor as tt"
+    "from theano import shared, tensor as tt\n",
+    "\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "plt.style.use('seaborn-darkgrid')\n",
+    "print('Running on PyMC3 v{}'.format(pm.__version__))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "plt.rc('animation', writer='avconv')\n",
+    "plt.rc('animation', writer='ffmpeg')\n",
     "blue, *_ = sns.color_palette()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [],
    "source": [
     "SEED = 972915 # from random.org; for reproducibility\n",
@@ -73,16 +75,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/fonnescj/anaconda3/envs/dev/lib/python3.6/site-packages/pandas/io/parsers.py:2108: FutureWarning: split() requires a non-empty pattern match.\n",
+      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/io/parsers.py:2218: FutureWarning: split() requires a non-empty pattern match.\n",
       "  yield pat.split(line.strip())\n",
-      "/Users/fonnescj/anaconda3/envs/dev/lib/python3.6/site-packages/pandas/io/parsers.py:2110: FutureWarning: split() requires a non-empty pattern match.\n",
+      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/io/parsers.py:2220: FutureWarning: split() requires a non-empty pattern match.\n",
       "  yield pat.split(line.strip())\n"
      ]
     }
@@ -100,25 +102,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
        "    }\n",
        "\n",
        "    .dataframe tbody tr th {\n",
        "        vertical-align: top;\n",
        "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
@@ -179,7 +181,7 @@
        "4    396 -0.059913      0.818631  -1.655168"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -197,14 +199,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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mQsdmQMdT7pIoEymOhO12Oz7++GNMnDgRALB//364XK6UN4yIzEvujGCbtS8T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\nIjLI/wcrR/3zLrMINQAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11dd1a358>"
+       "<matplotlib.figure.Figure at 0x118213668>"
       ]
      },
      "metadata": {},
@@ -238,10 +240,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [],
    "source": [
     "fig, (scatter_ax, hist_ax) = plt.subplots(ncols=2, figsize=(16, 6))\n",
@@ -289,14 +289,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
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+       "AQAAAAAAABlkAAAAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAQAAAAAAAAAAAAAAAAAA\n",
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+       "ABxkcmVmAAAAAAAAAAEAAAAMdXJsIAAAAAEAAAHYc3RibAAAALRzdHNkAAAAAAAAAAEAAACkYXZj\n",
+       "MQAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAASAAbAASAAAAEgAAAAAAAAAAQAAAAAAAAAAAAAAAAAA\n",
+       "AAAAAAAAAAAAAAAAAAAAAAAAABj//wAAADJhdmNDAWQAH//hABlnZAAfrNlASA3oQAAAAwBAAAAD\n",
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+       "AQAAoAAAAAABAABAAAAAAAEAAAAAAAAAAQAAIAAAAAABAACgAAAAAAEAAEAAAAAAAQAAAAAAAAAB\n",
+       "AAAgAAAAAAEAAKAAAAAAAQAAQAAAAAABAAAAAAAAAAEAACAAAAAAHHN0c2MAAAAAAAAAAQAAAAEA\n",
+       "AAANAAAAAQAAAEhzdHN6AAAAAAAAAAAAAAANAABNEgAAC8wAAAQmAAACEgAAAj8AAAmIAAAF/QAA\n",
+       "AlgAAAL1AAAH/wAABucAAALDAAADRQAAABRzdGNvAAAAAAAAAAEAAAAsAAAAYnVkdGEAAABabWV0\n",
+       "YQAAAAAAAAAhaGRscgAAAAAAAAAAbWRpcmFwcGwAAAAAAAAAAAAAAAAtaWxzdAAAACWpdG9vAAAA\n",
+       "HWRhdGEAAAABAAAAAExhdmY1Ny41Ni4xMDE=\n",
        "\">\n",
        "  Your browser does not support the video tag.\n",
        "</video>"
@@ -923,7 +947,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -961,10 +985,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def norm_cdf(z):\n",
@@ -978,12 +1000,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
-    "collapsed": true,
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING (theano.gof.cmodule): The same cache key is associated to different modules (/Users/jlao/.theano/compiledir_Darwin-17.5.0-x86_64-i386-64bit-i386-3.5.1-64/tmp6mq4zwxw and /Users/jlao/.theano/compiledir_Darwin-17.5.0-x86_64-i386-64bit-i386-3.5.1-64/tmprc22erpn). This is not supposed to happen! You may need to manually delete your cache directory to fix this.\n",
+      "WARNING (theano.gof.cmodule): The same cache key is associated to different modules (/Users/jlao/.theano/compiledir_Darwin-17.5.0-x86_64-i386-64bit-i386-3.5.1-64/tmp6mq4zwxw and /Users/jlao/.theano/compiledir_Darwin-17.5.0-x86_64-i386-64bit-i386-3.5.1-64/tmprc22erpn). This is not supposed to happen! You may need to manually delete your cache directory to fix this.\n"
+     ]
+    }
+   ],
    "source": [
     "N, _ = df.shape\n",
     "K = 20\n",
@@ -1026,10 +1056,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [],
    "source": [
     "with model:\n",
@@ -1047,10 +1075,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [],
    "source": [
     "with model:\n",
@@ -1067,7 +1093,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
     "scrolled": false
    },
@@ -1076,7 +1102,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 30000/30000 [01:49<00:00, 274.28it/s]\n"
+      "Sequential sampling (1 chains in 1 job)\n",
+      "CompoundStep\n",
+      ">Metropolis: [tau]\n",
+      ">Metropolis: [delta]\n",
+      ">Metropolis: [gamma]\n",
+      ">Metropolis: [beta]\n",
+      ">Metropolis: [alpha]\n",
+      "100%|██████████| 30000/30000 [02:44<00:00, 182.90it/s]\n",
+      "Only one chain was sampled, this makes it impossible to run some convergence checks\n"
      ]
     }
    ],
@@ -1086,7 +1120,7 @@
     "\n",
     "with model:\n",
     "    step = pm.Metropolis()\n",
-    "    trace = pm.sample(SAMPLES, step, tune=BURN, random_seed=SEED)\n",
+    "    trace = pm.sample(SAMPLES, step, chains=1, tune=BURN, random_seed=SEED)\n",
     "    "
    ]
   },
@@ -1099,14 +1133,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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xJxHxpD5klCRJDTS6UW7sc+qZeWNE3D1+hWmOPidJkvqoSanfERGvAZZTfbTt1gnWmc7oc5IkqY+alPoLgTdTnYa/Gjh0/Aodo8+d0TniXEQ8vl9BJUlSb5PO0paZtwOXUM2+tjwzb+ux+jcjYj+AiDgG+GhfUkqSpElNWuoR8RHgn6nuZn9x/Vn1bvYBXl+P//4wYJe+pJQkSZNqcvp9+8zcuX78/ohY0WPdx1BdU78IeBywJfDLmUWUJElNTHqkDqyMiIcDRMSDgF/3WPdE4BmZ+Uqqm+S+POOEkiSpkSalvgtwdUT8HLgO2Dcibo6ImyZYd8/MvB4gM1cAu/UtqSRJ6qnJMLHbTrZORHwhMw8EboyIsRHloBpd7iEziyhJkppock19UnWhk5lbTLauJEmaHX0p9TERcQBwCLBo7LXM3L+f+5AkSRNr8pG2Z4x7/rweq78X+ABwfMcfSZI0B7oeqddlvjvwgogYu+FtAfBM4HNd3vbTzLywrwklSVIjvU6/XwE8kGrQmaxfWw98usd7vhIRl1INJwtAZt5vWFlJktR/XUu9HsP94xHxifql+cCuwM96bO9IqjnUV/UtoSRJaqTJjXKnUB15bw08Hvgt8JIu6/4mMz/bp2ySJGkKmpT6EzLzdRHx3cx8ckSc32PdP0XEucDlVJ9RJzPf1I+gkiSptyalviAidgSui4iNgaU91v1af2JJkqSpalLqZwKnUs2j/m7gQ91WzMyP9ymXJEmaoibzqZ8K7Ef1cbYTMtM50iVJGkCTHqlHxHOBN9frfi4iRjPzpH7sPCLmU50F2AFYAxyWmSsnWOfrwFcy87R+7FeSpBI1maXtaKqZ2m4BTgL+qY/7fzawKDN3BY4DTp5gnZOAv+rjPiVJKlKTUl+XmWuA0cwcBf7Yx/3vAZwLf5mqdafOhRFxINWAN+f2cZ+SJBWpyY1yF0XEp4EtI+I04LI+7n9T4PaO5+siYmFmro2IRwMHAQcCb+m2gSVLNmHhwgV9CzQysrhv2+qHBQvmt5ZpOvttM+90mXn2DVteMPNcGLa8MPiZm8yn/qaIeCrwY+DqzDynj/u/g/t+RG5+Zq6tH78YeChwAfC3wJ8j4rrMvM9R++rVa/oYB1atuquv25upkZHFrWWazn7bzDtdZp59w5YXzDwXhi0vDEbmZcu6f7K8yY1ynUfJj4+I7YEbgM9m5j0zzHYxcADVDXi7AFeOLcjMYzsynEg1Wp2n4SVJ6qLJ6fcdqCZ1+T7VDXNbATcDTwFeNMP9nw3sGxGXAPOAQyLiaGBlZn51htuWJGmD0qTURzLzufXjD0XEeZn5ooi4aKY7z8z1wBHjXr5mgvVOnOm+JEkqXZO730ciYnOAiHggsFlEbAQM7p0CkiRtgJocqb8F+EFE3E51U9trgGMAR5aTJGmANDr9DmwHLAN+V39W3RvWJEkaME1K/fDMPItqHnVJkjSgmpT6JhFxOZBUo7uRmQfNaipJkjRlTUr9jbOeQpIkzViTu9+vBB4CbE01sttusxlIkiRNT5Mj9bOBq4HtgbuB4RrTT5KkDUSTI/V5mXkE1TX1fYG/nt1IkiRpOpqU+tqIWAQ8ABil2dG9JEmaY01K/T+B1wHnUU3kcu2sJpIkSdPSZOrVL449jojPA38zq4kkSdK0NDlS/4vMvAM4a5aySJKkGZhSqdfm9T2FJEmasemU+mjfU0iSpBnrek09Ij7N/Qt8HrDNrCaSJEnT0utGudOm+LokSWpR11LPzO/NZRBJkjQz07mmLkmSBpClLklSISx1SZIKYalLklQIS12SpEJY6pIkFcJSlySpEJa6JEmFsNQlSSqEpS5JUiEsdUmSCmGpS5JUCEtdkqRCWOqSJBXCUpckqRCWuiRJhbDUJUkqhKUuSVIhLHVJkgphqUuSVAhLXZKkQljqkiQVwlKXJKkQlrokSYWw1CVJKoSlLklSISx1SZIKYalLklQIS12SpEJY6pIkFcJSlySpEJa6JEmFsNQlSSqEpS5JUiEsdUmSCmGpS5JUiIVt7jwi5gOnAjsAa4DDMnNlx/KjgOfXT7+RmW+b+5SSJA2Hto/Unw0sysxdgeOAk8cWRMQ2wMHAbsAuwH4R8ZhWUkqSNATaLvU9gHMBMnMFsFPHshuAp2bmuswcBTYC7p77iJIkDYdWT78DmwK3dzxfFxELM3NtZt4D3BIR84D3AJdn5s/Hb2DJkk1YuHBB3wKNjCzu27b6YcGC+a1lms5+28w7XWaefcOWF8w8F4YtLwx+5rZL/Q5gacfz+Zm5duxJRCwCzgDuBF410QZWr17T10CrVt3V1+3N1MjI4tYyTWe/beadLjPPvmHLC2aeC8OWFwYj87JlS7sua/v0+8XA/gARsQtw5diC+gj9K8AVmfmKzFzXTkRJkoZD20fqZwP7RsQlwDzgkIg4GlgJLACeBGwSEU+r1z8+My9tJ6okSYOt1VLPzPXAEeNevqbj8aI5jCNJ0lBr+/S7JEnqE0tdkqRCWOqSJBXCUpckqRCWuiRJhbDUJUkqhKUuSVIhLHVJkgphqUuSVAhLXZKkQljqkiQVwlKXJKkQlrokSYWw1CVJKoSlLklSISx1SZIKYalLklQIS12SpEJY6pIkFcJSlySpEJa6JEmFsNQlSSqEpS5JUiEsdUmSCmGpS5JUCEtdkqRCWOqSJBXCUpckqRCWuiRJhbDUJUkqhKUuSVIhLHVJkgphqUuSVAhLXZKkQljqkiQVwlKXJKkQlrokSYWw1CVJKoSlLklSISx1SZIKYalLklQIS12SpEJY6pIkFcJSlySpEJa6JEmFsNQlSSqEpS5JUiEsdUmSCmGpS5JUCEtdkqRCWOqSJBXCUpckqRCWuiRJhbDUJUkqxMI2dx4R84FTgR2ANcBhmbmyY/nLgVcAa4GTMvOcVoJKkjQE2j5SfzawKDN3BY4DTh5bEBEPBo4EdgeeArwjIjZpJaUkSUOg1SN1YA/gXIDMXBERO3UseyJwcWauAdZExErgMcBlcx3yCScvn/E2Ljtmr1nZbrdtz4ZhyytJG5p5o6Ojre08Ij4CfDEzv1k//zWwTWaujYgXAttn5hvrZWcCZ2bmd1oLLEnSAGv79PsdwNKO5/Mzc22XZUuBVXMVTJKkYdN2qV8M7A8QEbsAV3Ys+yGwZ0QsiojNgEcCV819REmShkPbp9/H7n5/DDAPOISq5Fdm5lfru98Pp/rl4+2Z+cXWwkqSNOBaLfW5FBE7A+/KzL3bzjKZiFgAfBgIYBQ4IjMH/ixFRPyY6rIJwLWZeUibeSYTES8FXlo/XQQ8FnhwZg7kZZ760x//DWxD9X3+l8z8RbupupvoZy4i3gdkZp7WWrAuOvNGxKOA06kONn5B9XHbtT030IJxmR8HnEOVF+C/MvOz7aWb2LjMnwEeXC/6W2BFZj6/tXATGJf38cBpVB/B/gnw2sxc32rAcdq++31ORMSxwIuAP7adpaEDADJz94jYG/g34FmtJppERCwC5g3DL01jMvNjwMcAIuI/gTMGtdBrLwdWZ+YuERHAf1B93HPgjP+Zi4hlwJnAI4D3tBhtQhP8G/F24E2ZuTwiPkb1M3l2S/EmNEHmHYFTMvPk7u9q1/jMYwUeEX8FfBc4qr109zfB9/h04MjMvCQiTgIOAj7ZVr6JtH1Nfa78EnhO2yGayswvU112ANia4bhBcAdgcUScFxEX1PdIDIX6o5R/n5mnt51lEo8CvgnVoS7VfSaDavzP3BLgROATraSZ3Pi8z60LfWOqI8nb24nV0/jMOwJPj4jlEfHRiFja5X1t6vZv8duAD2bmzXOcZzLj826ZmZfUjy+m+lj2QNkgSr2+Fn9P2zmmov5Y38eBDwJntZ2ngbuA91IdOR4BnBURw3Im6E1U/6gMup8Az4iIefUvTQ+tL9UMnPE/c5l5bWb+oMVIPU2Qd11EbA38FNgcuKKtbN1M8O/aD4E3ZOZewK+At7YSrIeJ/i2OiAcB+1CfNRskE+T9VUQ8qX58APCAuU/V2wZR6sMqM19CdbrywxExcH95xvk58MnMHM3MnwN/ALZoOdOkImIEiMz8bttZGjiD6lr694F/An6UmevajVSuzLw+M7ejuoZ6Stt5Gjg7M3809hh4XJthpuBA4FND8nf5EOD4iDgf+B1wS8t57sdSH0AR8aKIOL5+ehewvv4zyA6lHuY3Ih4CbAoM2qm0iewFnN92iIaeAJyfmXsAn6c6GtMsiIivRsR29dM7GfyfP4BvRcQT68f7AD/qtfIA+Ufqy0pD4OnAwZm5D/BA4Nst57mfYTk9uqH5EvDfEbEc2Ah4XWb+qeVMk/ko8LGIuIjqjv1DB/Fu4QkEw1OOvwD+NSJOoLrP4mUt5ynZO6n+Pv+Z6hfrw1rO08QrgQ9GxD3Ab7j3vpxBN2w/g+dHxF3AdzPzG20HGm+D+UibJEml8/S7JEmFsNQlSSqEpS5JUiEsdUmSCmGpS5JUCEtdGiARsXdEjEbE88e9/v/qMciJiC/1eP/2EbHXLMccCn4vtCGy1KXBcw3wl1KPiO3pGI4yM3vNY/BcqjHi5fdCGyAHn5EGzxVARMRmmXk78EKq8f8fRrXgN8CWwHKqMet/AlwA7E81leyf62lwPwf8XWbeHRHvpPpl4TrgXcCfqWac+jXVLIDrqCaveEVm/mWs63pUtY8AG1MNwvJ8ql8wzqD692OUataqKyJiJXAJ1dDG5wObAU+kmn/mRfWZhnnAVlQTvLw4M6+JiGPq7a4FlmfmGyPiRODhwIOoJjU6KjO/VY+7fZ+8wMH1f/tiYNv6v+/bnd+LzPzhNP9fSEPFI3VpMH0ReE5EzKMqxks6F9aj9R1ENTTvJ4HXZ+b1VJNinDJJiS3KzD3r930YeE5mPgm4kXvnlx/zXuAdmbkr8H6q8cTfC7y/njjktVSjCUI1H/abgT2BI4FTgZ2BPeox9gF+mZn/QDVj27vrsxDPA3ar/2wXEc+o112TmU+r93FU/b3olnezzHwG8EzguMy8seH3QiqKpS4Npk9RHb3uRTWBy/1k5nXARVRHs+dOsr15nW+tvy6jmnTncxFxIbAf1VFxpwAurff31cw8j2rK1+X1az+hOvIG+ENm/ro+0v9jZv4sM0eppi1dVK9zQf31knrbfwesyMx76nW/D/x9vc7l9dcb6vf3yvuTcetKGyRLXRpAmfkrqtPcR1IdUd9PPf3qo6kK9pj65fXc+3N9N7BFfYT72I63jk1OcgvwP8CzMnNvqtPaF3BfV1NNJENEHBwRr6lf27N+7bFU44xDdSp+MjvWX3enmtb0GmDniFhY59yLasa/ibbXK+9E++78XkgbBP/CS4Prs8BW9VS29xERm1Gd9j4UeAPwoojYiWpmrldHxJOBdwPfqP/cNn4bmbme6tT21yPiEuBVwFXjVnsD1VSTF1Jduz4LeD3wmnrCof9iahPLPC0iLgCOBY7JzCuprv1fTDUf+HXAlyd6Y8O8nTq/F9IGwQldJM2J+ka5z2TmZJcKJE2TR+qSJBXCI3VJkgrhkbokSYWw1CVJKoSlLklSISx1SZIKYalLklQIS12SpEL8f7BgamXydvsxAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x13004cd68>"
+       "<matplotlib.figure.Figure at 0x119470cf8>"
       ]
      },
      "metadata": {},
@@ -1137,7 +1171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {
     "scrolled": false
    },
@@ -1146,7 +1180,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 5000/5000 [00:28<00:00, 178.45it/s]\n"
+      "100%|██████████| 5000/5000 [00:33<00:00, 151.39it/s]\n"
      ]
     }
    ],
@@ -1169,14 +1203,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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i7S0ekWN84wybLI2OOdYRvZ3e0WxqhIxOUIOAClvrEu9zthVpgDq3UXKGMKFV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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x13016b630>"
+       "<matplotlib.figure.Figure at 0x19e359dd8>"
       ]
      },
      "metadata": {},
@@ -1184,7 +1218,7 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots()\n",
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
     "\n",
     "ax.scatter(df.std_range, df.std_logratio,\n",
     "           c=blue, zorder=10,\n",
@@ -1217,7 +1251,9 @@
     "\n",
     "To learn more about depdendent density regression and related models, consult [_Bayesian Data Analysis_](http://www.stat.columbia.edu/~gelman/book/), [_Bayesian Nonparametric Data Analysis_](http://www.springer.com/us/book/9783319189673), or [_Bayesian Nonparametrics_](https://www.google.com/webhp?sourceid=chrome-instant&ion=1&espv=2&ie=UTF-8#q=bayesian+nonparametrics+book).\n",
     "\n",
-    "This example first appeared [here](http://austinrochford.com/posts/2017-01-18-ddp-pymc3.html)."
+    "This example first appeared [here](http://austinrochford.com/posts/2017-01-18-ddp-pymc3.html).\n",
+    "\n",
+    "Author: [Austin Rochford](https://github.com/AustinRochford/)"
    ]
   }
  ],
@@ -1237,7 +1273,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.5.1"
   },
   "widgets": {
    "state": {},
diff --git a/pymc3/distributions/mixture.py b/pymc3/distributions/mixture.py
index ebc9c2144d..7f040a1ee2 100644
--- a/pymc3/distributions/mixture.py
+++ b/pymc3/distributions/mixture.py
@@ -158,18 +158,33 @@ def random_choice(*args, **kwargs):
                 return np.random.choice(k, p=w, *args, **kwargs)
 
         w = draw_values([self.w], point=point)[0]
-
+        comp_tmp = self._comp_samples(point=point, size=None)
+        if self.shape.size == 0:
+            distshape = np.asarray(np.broadcast(w, comp_tmp).shape)[..., :-1]
+        else:
+            distshape = self.shape
         w_samples = generate_samples(random_choice,
                                      w=w,
                                      broadcast_shape=w.shape[:-1] or (1,),
-                                     dist_shape=self.shape,
+                                     dist_shape=distshape,
                                      size=size).squeeze()
-        comp_samples = self._comp_samples(point=point, size=size)
-
-        if comp_samples.ndim > 1:
-            return np.squeeze(comp_samples[np.arange(w_samples.size), w_samples])
+        if (size is None) or (distshape.size == 0):
+            comp_samples = self._comp_samples(point=point, size=size)
+            if comp_samples.ndim > 1:
+                samples = np.squeeze(comp_samples[np.arange(w_samples.size), ..., w_samples])
+            else:
+                samples = np.squeeze(comp_samples[w_samples])
         else:
-            return np.squeeze(comp_samples[w_samples])
+            samples = np.zeros((size,)+tuple(distshape))
+            for i in range(size):
+                w_tmp = w_samples[i, :]
+                comp_tmp = self._comp_samples(point=point, size=None)
+                if comp_tmp.ndim > 1:
+                    samples[i, :] = np.squeeze(comp_tmp[np.arange(w_tmp.size), ..., w_tmp])
+                else:
+                    samples[i, :] = np.squeeze(comp_tmp[w_tmp])
+
+        return samples
 
 
 class NormalMixture(Mixture):
@@ -197,21 +212,21 @@ class NormalMixture(Mixture):
         the component standard deviations
     tau : array of floats
         the component precisions
+    distshape : shape of the Normal component
+        notice that it should be different than the shape
+        of the mixture distribution, with one axis being
+        the number of component.
 
     Note: You only have to pass in sd or tau, but not both.
     """
 
-    def __init__(self, w, mu, *args, **kwargs):
+    def __init__(self, w, mu, distshape=(), *args, **kwargs):
         _, sd = get_tau_sd(tau=kwargs.pop('tau', None),
                            sd=kwargs.pop('sd', None))
 
-        distshape = np.broadcast(mu, sd).shape
         self.mu = mu = tt.as_tensor_variable(mu)
         self.sd = sd = tt.as_tensor_variable(sd)
 
-        if not distshape:
-            distshape = np.broadcast(mu.tag.test_value, sd.tag.test_value).shape
-
         super(NormalMixture, self).__init__(w, Normal.dist(mu, sd=sd, shape=distshape),
                                             *args, **kwargs)
 
diff --git a/pymc3/tests/test_distributions_random.py b/pymc3/tests/test_distributions_random.py
index cf361c8d36..bce491782d 100644
--- a/pymc3/tests/test_distributions_random.py
+++ b/pymc3/tests/test_distributions_random.py
@@ -754,17 +754,79 @@ def ref_rand(size, w, mu, sd):
         pymc3_random(pm.NormalMixture, {'w': Simplex(2),
                      'mu': Domain([[.05, 2.5], [-5., 1.]], edges=(None, None)),
                      'sd': Domain([[1, 1], [1.5, 2.]], edges=(None, None))},
+                     extra_args={'distshape': 2},
                      size=1000,
                      ref_rand=ref_rand)
         pymc3_random(pm.NormalMixture, {'w': Simplex(3),
                      'mu': Domain([[-5., 1., 2.5]], edges=(None, None)),
                      'sd': Domain([[1.5, 2., 3.]], edges=(None, None))},
+                     extra_args={'distshape': 3},
                      size=1000,
                      ref_rand=ref_rand)
 
+
+def test_mixture_random_shape():
+    # test the shape broadcasting in mixture random
+    y = np.concatenate([nr.poisson(5, size=10),
+                        nr.poisson(9, size=10)])
+    with pm.Model() as m:
+        comp0 = pm.Poisson.dist(mu=np.ones(2))
+        w0 = pm.Dirichlet('w0', a=np.ones(2))
+        like0 = pm.Mixture('like0',
+                           w=w0,
+                           comp_dists=comp0,
+                           shape=y.shape,
+                           observed=y)
+
+        comp1 = pm.Poisson.dist(mu=np.ones((20, 2)),
+                                shape=(20, 2))
+        w1 = pm.Dirichlet('w1', a=np.ones(2))
+        like1 = pm.Mixture('like1',
+                           w=w1,
+                           comp_dists=comp1, observed=y)
+
+        comp2 = pm.Poisson.dist(mu=np.ones(2))
+        w2 = pm.Dirichlet('w2',
+                          a=np.ones(2),
+                          shape=(20, 2))
+        like2 = pm.Mixture('like2',
+                           w=w2,
+                           comp_dists=comp2,
+                           observed=y)
+
+        comp3 = pm.Poisson.dist(mu=np.ones(2),
+                                shape=(20, 2))
+        w3 = pm.Dirichlet('w3',
+                          a=np.ones(2),
+                          shape=(20, 2))
+        like3 = pm.Mixture('like3',
+                           w=w3,
+                           comp_dists=comp3,
+                           observed=y)
+
+    rand0 = like0.distribution.random(m.test_point, size=100)
+    assert rand0.shape == (100, 20)
+
+    rand1 = like1.distribution.random(m.test_point, size=100)
+    assert rand1.shape == (100, 20)
+
+    rand2 = like2.distribution.random(m.test_point, size=100)
+    assert rand2.shape == (100, 20)
+
+    rand3 = like3.distribution.random(m.test_point, size=100)
+    assert rand3.shape == (100, 20)
+
+    with m:
+        ppc = pm.sample_ppc([m.test_point], samples=200)
+    assert ppc['like0'].shape == (200, 20)
+    assert ppc['like1'].shape == (200, 20)
+    assert ppc['like2'].shape == (200, 20)
+    assert ppc['like3'].shape == (200, 20)
+
+
 def test_density_dist_with_random_sampleable():
     with pm.Model() as model:
-        mu = pm.Normal('mu',0,1)
+        mu = pm.Normal('mu', 0, 1)
         normal_dist = pm.Normal.dist(mu, 1)
         pm.DensityDist('density_dist', normal_dist.logp, observed=np.random.randn(100), random=normal_dist.random)
         trace = pm.sample(100)
@@ -776,7 +838,7 @@ def test_density_dist_with_random_sampleable():
 
 def test_density_dist_without_random_not_sampleable():
     with pm.Model() as model:
-        mu = pm.Normal('mu',0,1)
+        mu = pm.Normal('mu', 0, 1)
         normal_dist = pm.Normal.dist(mu, 1)
         pm.DensityDist('density_dist', normal_dist.logp, observed=np.random.randn(100))
         trace = pm.sample(100)
diff --git a/pymc3/tests/test_sampling.py b/pymc3/tests/test_sampling.py
index ef9d85a78f..a781d88402 100644
--- a/pymc3/tests/test_sampling.py
+++ b/pymc3/tests/test_sampling.py
@@ -140,12 +140,14 @@ def test_empty_model():
             pm.sample()
         error.match('any free variables')
 
+
 def test_partial_trace_sample():
     with pm.Model() as model:
         a = pm.Normal('a', mu=0, sd=1)
         b = pm.Normal('b', mu=0, sd=1)
         trace = pm.sample(trace=[a])
 
+
 @pytest.mark.xfail(condition=(theano.config.floatX == "float32"), reason="Fails on float32")
 class TestNamedSampling(SeededTest):
     def test_shared_named(self):
@@ -235,7 +237,34 @@ def test_normal_vector(self):
             trace = pm.sample()
 
         with model:
-	    # test list input
+            # test list input
+            ppc0 = pm.sample_ppc([model.test_point], samples=10)
+            ppc = pm.sample_ppc(trace, samples=10, vars=[])
+            assert len(ppc) == 0
+            ppc = pm.sample_ppc(trace, samples=10, vars=[a])
+            assert 'a' in ppc
+            assert ppc['a'].shape == (10, 2)
+
+            ppc = pm.sample_ppc(trace, samples=10, vars=[a], size=4)
+            assert 'a' in ppc
+            assert ppc['a'].shape == (10, 4, 2)
+
+    def test_vector_observed(self):
+        # This test was initially created to test whether observedRVs
+        # can assert the shape automatically from the observed data.
+        # It can make sample_ppc correct for RVs similar to below (i.e.,
+        # some kind of broadcasting is involved). However, doing so makes
+        # the application with `theano.shared` array as observed data
+        # invalid (after the `.set_value` the RV shape could change).
+        with pm.Model() as model:
+            mu = pm.Normal('mu', mu=0, sd=1)
+            a = pm.Normal('a', mu=mu, sd=1,
+                          shape=2,  # necessary to make ppc sample correct
+                          observed=np.array([0., 1.]))
+            trace = pm.sample()
+
+        with model:
+            # test list input
             ppc0 = pm.sample_ppc([model.test_point], samples=10)
             ppc = pm.sample_ppc(trace, samples=10, vars=[])
             assert len(ppc) == 0
@@ -254,7 +283,7 @@ def test_sum_normal(self):
             trace = pm.sample()
 
         with model:
-	    # test list input
+            # test list input
             ppc0 = pm.sample_ppc([model.test_point], samples=10)
             ppc = pm.sample_ppc(trace, samples=1000, vars=[b])
             assert len(ppc) == 1
@@ -263,6 +292,7 @@ def test_sum_normal(self):
             _, pval = stats.kstest(ppc['b'], stats.norm(scale=scale).cdf)
             assert pval > 0.001
 
+
 class TestSamplePPCW(SeededTest):
     def test_sample_ppc_w(self):
         data0 = np.random.normal(0, 1, size=500)


Code review comments:
diff_hunk: @@ -197,21 +212,21 @@ class NormalMixture(Mixture):
         the component standard deviations
     tau : array of floats
         the component precisions
+    distshape : shape of the Normal component
path: pymc3/distributions/mixture.py
text: @user1:
it looks like this is already called `dist_shape` in `generate_samples` (instead of `distshape`)... is it easy to change that here as well?

@author:
I will change it to `comp_shape` to avoid confusion.

diff_hunk: @@ -197,21 +212,21 @@ class NormalMixture(Mixture):
         the component standard deviations
     tau : array of floats
         the component precisions
+    distshape : shape of the Normal component
+        notice that it should be different than the shape
+        of the mixture distribution, with one axis being
+        the number of component.
path: pymc3/distributions/mixture.py
text: @user1:
small typo: "number of component**s**".  I am also not sure I understand the docstring either -- would adding "a mixture of three 2d normal distributions would have `shape=(3, 2)` and `dist_shape=(2,)`" be accurate?

@author:
Yeah this is a tricky one.
"a mixture of three 2d normal distributions would have `shape=(3, 2)` and `dist_shape=(2,)`" this is not correct. A mixture of three 2d normal distributions would have `shape=(..., 2)`, and `dist_shape=(2, 3)`. But I am not sure multi-D normal mixture actually works... 
In practice, if you have a NormalMixture RV that is `shape=(a,b,c,...)`, the component shape get one more axis `comp_shape=(a,b,c,...,k)`, with `k` components. However, passing `comp_shape=(a,b,c,...,k)` (also what the previous code trying to do) break cases when you are using `theano.shared` observed and sample_ppc.