Bayesian inference begins with specification of a probability model relating
unknown variables to data. PyMC provides three basic building blocks for
Bayesian probability models: `Stochastic`, `Deterministic` and
`Potential`.

A `Stochastic` object represents a variable whose value is not completely
determined by its parents, and a `Deterministic` object represents a variable
that is entirely determined by its parents. In object-oriented programming
parlance, `Stochastic` and `Deterministic` are subclasses of the
`Variable` class, which only serves as a template for other classes and is
never actually implemented in models.

The third basic class, `Potential`, represents ‘factor potentials’
([Lauritzen1990],[Jordan2004]_), which are *not* variables but simply
log-likelihood terms and/or constraints that are multiplied into joint
distributions to modify them. `Potential` and `Variable` are subclasses of
`Node`.

PyMC probability models are simply linked groups of `Stochastic`,
`Deterministic` and `Potential` objects. These objects have very limited
awareness of the models in which they are embedded and do not themselves
possess methods for updating their values in fitting algorithms. Objects
responsible for fitting probability models are described in chapter
*Fitting Models*.

A stochastic variable has the following primary attributes:

`value`:- The variable’s current value.
`logp`:- The log-probability of the variable’s current value given the values of its parents.

A stochastic variable can optionally be endowed with a method called
`random`, which draws a value for the variable given the values of its
parents [1]. Stochastic objects have the following additional attributes:

`parents`:- A dictionary containing the variable’s parents. The keys of the dictionary
correspond to the names assigned to the variable’s parents by the variable,
and the values correspond to the actual parents. For example, the keys of
\(s\)‘s parents dictionary in model ((?)) would be
`'t_l'`and`'t_h'`. Thanks to Python’s dynamic typing, the actual parents (*i.e.*the values of the dictionary) may be of any class or type. `children`:- A set containing the variable’s children.
`extended_parents`:- A set containing all the stochastic variables on which the variable depends either directly or via a sequence of deterministic variables. If the value of any of these variables changes, the variable will need to recompute its log-probability.
`extended_children`:- A set containing all the stochastic variables and potentials that depend on the variable either directly or via a sequence of deterministic variables. If the variable’s value changes, all of these variables will need to recompute their log-probabilities.
`observed`:- A flag (boolean) indicating whether the variable’s value has been observed (is fixed).
`dtype`:- A NumPy dtype object (such as
`numpy.int`) that specifies the type of the variable’s value to fitting methods. If this is`None`(default) then no type is enforced.

There are three main ways to create stochastic variables, called the
**automatic**, **decorator**, and **direct** interfaces.

**Automatic**Stochastic variables with standard distributions provided by PyMC (see chapter

*Probability distributions*) can be created in a single line using special subclasses of`Stochastic`. For example, the uniformly-distributed discrete variable \(switchpoint\) in ((?)) is created using the automatic interface as follows:switchpoint = DiscreteUniform('switchpoint', lower=0, upper=110, doc='Switchpoint[year]')

In addition to the classes in chapter

*Probability distributions*,`scipy.stats.distributions`‘ random variable classes are wrapped as`Stochastic`subclasses if SciPy is installed. These distributions are in the submodule`pymc.SciPyDistributions`.Users can call the class factory

`stochastic_from_dist`to produce`Stochastic`subclasses of their own from probability distributions not included with PyMC.**Decorator**Uniformly-distributed discrete stochastic variable \(switchpoint\) in ((?)) could alternatively be created from a function that computes its log-probability as follows:

@pymc.stochastic(dtype=int) def switchpoint(value=1900, t_l=1851, t_h=1962): """The switchpoint for the rate of disaster occurrence.""" if value > t_h or value < t_l: # Invalid values return -numpy.inf else: # Uniform log-likelihood return -numpy.log(t_h - t_l + 1)

Note that this is a simple Python function preceded by a Python expression called a

**decorator**[vanRossum2010], here called`@stochastic`. Generally, decorators enhance functions with additional properties or functionality. The`Stochastic`object produced by the`@stochastic`decorator will evaluate its log-probability using the function \(switchpoint\). The`value`argument, which is required, provides an initial value for the variable. The remaining arguments will be assigned as parents of \(switchpoint\) (*i.e.*they will populate the`parents`dictionary).To emphasize, the Python function decorated by

`@stochastic`should compute the*log*-density or*log*-probability of the variable. That is why the return value in the example above is \(-\log(t_h-t_l+1)\) rather than \(1/(t_h-t_l+1)\).The

`value`and parents of stochastic variables may be any objects, provided the log-probability function returns a real number (`float`). PyMC and SciPy both provide implementations of several standard probability distributions that may be helpful for creating custom stochastic variables.The decorator stochastic can take any of the arguments

`Stochastic.__init__`takes except`parents`,`logp`,`random`,`doc`and`value`. These arguments include`trace`,`plot`,`verbose`,`dtype`,`rseed`and`name`. The decorator interface has a slightly more complex implementation which allows you to specify a`random`method for sampling the stochastic variable’s value conditional on its parents.@pymc.stochastic(dtype=int) def switchpoint(value=1900, t_l=1851, t_h=1962): """The switchpoint for the rate of disaster occurrence.""" def logp(value, t_l, t_h): if value > t_h or value < t_l: return -numpy.inf else: return -numpy.log(t_h - t_l + 1) def random(t_l, t_h): from numpy.random import random return numpy.round( (t_l - t_h) * random() ) + t_l

The stochastic variable again gets its name, docstring and parents from function \(switchpoint\), but in this case it will evaluate its log-probability using the

`logp`function. The`random`function will be used when`switchpoint.random()`is called. Note that`random`doesn’t take a`value`argument, as it generates values itself.**Direct**It’s possible to instantiate

`Stochastic`directly:def switchpoint_logp(value, t_l, t_h): if value > t_h or value < t_l: return -numpy.inf else: return -numpy.log(t_h - t_l + 1) def switchpoint_rand(t_l, t_h): from numpy.random import random return numpy.round( (t_l - t_h) * random() ) + t_l switchpoint = Stochastic( logp = switchpoint_logp, doc = 'The switchpoint for the rate of disaster occurrence.', name = 'switchpoint', parents = {'t_l': 1851, 't_h': 1962}, random = switchpoint_rand, trace = True, value = 1900, dtype=int, rseed = 1., observed = False, cache_depth = 2, plot=True, verbose = 0)

Notice that the log-probability and random variate functions are specified externally and passed to

`Stochastic`as arguments. This is a rather awkward way to instantiate a stochastic variable; consequently, such implementations should be rare.

A Warning: Don’t update stochastic variables’ values in-place

`Stochastic` objects’ values should not be updated in-place. This
confuses PyMC’s caching scheme and corrupts the process used for accepting
or rejecting proposed values in the MCMC algorithm. The only way a
stochastic variable’s value should be updated is using statements of the
following form:

```
A.value = new_value
```

The following are in-place updates and should _never_ be used:

```
* ``A.value += 3``
* ``A.value[2,1] = 5``
* ``A.value.attribute = new_attribute_value``
```

This restriction becomes onerous if a step method proposes values for the elements of an array-valued variable separately. In this case, it may be preferable to partition the variable into several scalar-valued variables stored in an array or list.

Data are represented by `Stochastic` objects whose `observed` attribute is
set to `True`. If a stochastic variable’s `observed` flag is `True`, its
value cannot be changed, and it won’t be sampled by the fitting method.

In each interface, an optional keyword argument `observed` can be set to
`True`. In the decorator interface, this argument is added to the
`@stochastic` decorator:

`@pymc.stochastic(observed=True)`

In the other interfaces, the `observed=True` argument is added to the
instantiation of the `Stochastic`, or its subclass:

```
x = pymc.Binomial('x', n=n, p=p, observed=True)
```

Alternatively, in the decorator interface only, a `Stochastic` object’s
`observed` flag can be set to true by stacking an `@observed` decorator on
top of the `@stochastic` decorator:

```
@pymc.observed(dtype=int)
def ...
```

The `Deterministic` class represents variables whose values are completely
determined by the values of their parents. For example, in model
((?)), \(rate\) is a `deterministic` variable. Recall it
was defined by

\[\begin{eqnarray*}
r_t=\left\{\begin{array}{ll}
e & t\le s\\ l & t>s
\end{array}\right.,
\end{eqnarray*}\]

so \(rate\)‘s value can be computed exactly from the values of its parents \(early_mean\), \(late_mean\) and \(switchpoint\).

A `deterministic` variable’s most important attribute is `value`, which
gives the current value of the variable given the values of its parents. Like
`Stochastic`‘s `logp` attribute, this attribute is computed on-demand and
cached for efficiency.

A Deterministic variable has the following additional attributes:

`parents`:- A dictionary containing the variable’s parents. The keys of the dictionary correspond to the names assigned to the variable’s parents by the variable, and the values correspond to the actual parents.
`children`:- A set containing the variable’s children, which must be nodes.

Deterministic variables have no methods.

Deterministic variables are less complicated than stochastic variables, and
have similar **automatic**, **decorator**, and **direct** interfaces:

**Automatic**A handful of common functions have been wrapped in Deterministic objects. These are brief enough to list:

`LinearCombination`:- Has two parents \(x\) and \(y\), both of which must be iterable
(
*i.e.*vector-valued). This function returns: `Index`:Has two parents \(x\) and

`index`. \(x\) must be iterables,`index`must be valued as an integer. The value of an`index`is:`Index`is useful for implementing dynamic models, in which the parent-child connections change.`Lambda`:- Converts an anonymous function (in Python, called
**lambda functions**) to a`Deterministic`instance on a single line. `CompletedDirichlet`:- PyMC represents Dirichlet variables of length \(k\) by the first
\(k-1\) elements; since they must sum to 1, the \(k^{th}\)
element is determined by the others.
`CompletedDirichlet`appends the \(k^{th}\) element to the value of its parent \(D\). `Logit`,`InvLogit`,`StukelLogit`,`StukelInvLogit`:- Common link functions for generalized linear models, and their inverses.

It’s a good idea to use these classes when feasible in order to give hints to step methods.

**Elementary operations on variables**Certain elementary operations on variables create deterministic variables. For example:

>>> x = pymc.MvNormalCov('x',numpy.ones(3),numpy.eye(3)) >>> y = pymc.MvNormalCov('y',numpy.ones(3),numpy.eye(3)) >>> print x+y <pymc.PyMCObjects.Deterministic '(x_add_y)' at 0x105c3bd10> >>> print x[0] <pymc.CommonDeterministics.Index 'x[0]' at 0x105c52390> >>> print x[1]+y[2] <pymc.PyMCObjects.Deterministic '(x[1]_add_y[2])' at 0x105c52410>

All the objects thus created have

`trace=False`and`plot=False`by default. This convenient method of generating simple deterministics was inspired by [Kerman2004].**Decorator**A deterministic variable can be created via a decorator in a way very similar to

`Stochastic`‘s decorator interface:@deterministic(plot=False) def rate(s=switchpoint, e=early_mean, l=late_mean): ''' Concatenate Poisson means ''' out = empty(len(disasters_array)) out[:s] = e out[s:] = l return out

Notice that rather than returning the log-probability, as is the case for

`Stochastic`objects, the function returns the value of the deterministic object, given its parents. This return value may be of any type, as is suitable for the problem at hand. Also notice that, unlike for`Stochastic`objects, there is no`value`argument passed, since the value is calculated deterministically by the function itself. Arguments’ keys and values are converted into a parent dictionary as with`Stochastic`‘s short interface. The`deterministic`decorator can take`trace`,`verbose`and`plot`arguments, like the`stochastic`decorator [2].**Direct**`Deterministic`objects can also be instantiated directly:def rate_eval(switchpoint = s, early_rate = e, late_rate = l): value = zeros(N) value[:switchpoint] = early_rate value[switchpoint:] = late_rate return value rate = pymc.Deterministic(eval = rate_eval, name = 'rate', parents = {'switchpoint': switchpoint, 'early_mean': early_mean, 'late_mean': late_mean}), doc = 'The rate of disaster occurrence.', trace = True, verbose = 0, dtype=float, plot=False, cache_depth = 2)

In some situations it would be inconvenient to assign a unique label to each parent of some variable. Consider \(y\) in the following model:

\[\begin{align*}
x_0 &\sim N (0,\tau_x)\\
x_{i+1}|x_i &\sim \text{N}(x_i, \tau_x)\\
&i=0,\ldots, N-2\\
y|x &\sim N \left(\sum_{i=0}^{N-1}x_i^2,\tau_y\right)
\end{align*}\]

Here, \(y\) depends on every element of the Markov chain \(x\), but we
wouldn’t want to manually enter \(N\) parent labels ``x_0'`, ``x_1'`,
etc.

This situation can be handled naturally in PyMC:

```
N = 10
x_0 = pymc.Normal(`x_0', mu=0, tau=1)
x = numpy.empty(N, dtype=object)
x[0] = x_0
for i in range(1, N):
x[i] = pymc.Normal(`x_%i' % i, mu=x[i-1], tau=1)
@pymc.observed
def y(value=1, mu=x, tau=100):
return pymc.normal_like(value, numpy.sum(mu**2), tau)
```

PyMC automatically wraps array \(x\) in an appropriate `Container` class.
The expression `'x_%i' % i` labels each `Normal` object in the container
with the appropriate index \(i\). For example, if `i=1`, the name of the
corresponding element becomes ``x_1'`.

Containers, like variables, have an attribute called `value`. This attribute
returns a copy of the (possibly nested) iterable that was passed into the
container function, but with each variable inside replaced with its
corresponding value.

Containers can be constructed from lists, tuples, dictionaries, Numpy arrays,
modules, sets or any object with a `__dict__` attribute. Variables and
non-variables can be freely mixed in these containers, and different types of
containers can be nested [3]. Containers attempt to behave like the objects
they wrap. All containers are subclasses of `ContainerBase`.

Containers have the following useful attributes in addition to `value`:

variablesstochasticspotentialsdeterministicsdata_stochasticsstep_methods.

Each of these attributes is a set containing all the objects of each type in a container, and within any containers in the container.

The joint density corresponding to model ((?)) can be written as follows:

\[\begin{eqnarray*}
p(D,s,l,e) = p(D|s,l,e) p(s) p(l) p(e).
\end{eqnarray*}\]

Each factor in the joint distribution is a proper, normalized probability
distribution for one of the variables conditional on its parents. Such factors
are contributed by `Stochastic` objects.

In some cases, it’s nice to be able to modify the joint density by incorporating terms that don’t correspond to probabilities of variables conditional on parents, for example:

\[\begin{eqnarray*}
p(x_0, x_2, \ldots x_{N-1}) \propto \prod_{i=0}^{N-2} \psi_i(x_i, x_{i+1}).
\end{eqnarray*}\]

In other cases we may want to add probability terms to existing models. For example, suppose we want to constrain the difference between \(e\) and \(l\) in ((?)) to be less than 1, so that the joint density becomes

\[\begin{eqnarray*}
p(D,s,l,e) \propto p(D|s,l,e) p(s) p(l) p(e) I(|e-l|<1).
\end{eqnarray*}\]

Its possible to express this constraint by adding variables to the model, or by grouping \(e\) and \(l\) to form a vector-valued variable, but it’s uncomfortable to do so.

Arbitrary factors such as \(\psi\) and the indicator function
\(I(|e-l|<1)\) are implemented by objects of class `Potential` (from
[Lauritzen1990] and [Jordan2004], who call these terms ‘factor
potentials’). Bayesian hierarchical notation (cf model ((?)))
doesn’t accomodate these potentials. They are often used in cases where there
is no natural dependence hierarchy, such as the first example above (which is
known as a Markov random field). They are also useful for expressing ‘soft
data’ [Christakos2002] as in the second example below.

Potentials have one important attribute, `logp`, the log of their current
probability or probability density value given the values of their parents. The
only other additional attribute of interest is `parents`, a dictionary
containing the potential’s parents. Potentials have no methods. They have no
`trace` attribute, because they are not variables. They cannot serve as
parents of variables (for the same reason), so they have no `children`
attribute.

The role of potentials can be confusing, so we will provide another example: we have a dataset \(t\) consisting of the days on which several marked animals were recaptured. We believe that the probability \(S\) that an animal is not recaptured on any given day can be explained by a covariate vector \(x\). We model this situation as follows:

\[\begin{eqnarray*}
t_i|S_i \sim \text{Geometric}(S_i), & i=1\ldots N\\
S_i = \text{logit}^{-1}(\beta x_i), &i=1\ldots N\\
\beta\sim \text{N}(\mu_\beta, V_\beta).
\end{eqnarray*}\]

Now suppose we have some knowledge of other related experiments and we have a good idea of what \(S\) will be independent of the value of \(\beta\). It’s not obvious how to work this ‘soft data’, because as we’ve written the model \(S\) is completely determined by \(\beta\). There are three options within the strict Bayesian hierarchical framework:

- Express the soft data as an informative prior on \(\beta\).
- Incorporate the data from the previous experiments explicitly into the model.
- Refactor the model so that \(S\) is at the bottom of the hierarchy, and assign the prior directly.

Factor potentials provide a convenient way to incorporate the soft data without the need for such major modifications. We can simply modify the joint distribution from

\[\begin{eqnarray*}
p(t|S(x,\beta)) p(\beta)
\end{eqnarray*}\]

to

\[\begin{eqnarray*}
\gamma(S) p(t|S(x,\beta)) p(\beta),
\end{eqnarray*}\]

where the value of \(\gamma\) is large if \(S\)‘s value is plausible (based on our external information) and small otherwise. We do not know the normalizing constant for the new distribution, but we don’t need it to use most popular fitting algorithms. It’s a good idea to check the induced priors on \(S\) and \(\beta\) for sanity. This can be done in PyMC by fitting the model with the data \(t\) removed.

Its important to understand that \(\gamma\) is not a variable, so it does not have a value. That means, among other things, there will be no \(\gamma\) column in MCMC traces.

There are two ways to create potentials:

**Decorator**A potential can be created via a decorator in a way very similar to

`Deterministic`‘s decorator interface:@pymc.potential def psi_i(x_lo = x[i], x_hi = x[i+1]): """A pair potential""" return -(xlo - xhi)**2

The function supplied should return the potential’s current

*log*-probability or*log*-density as a Numpy`float`. The`potential`decorator can take`verbose`and`cache_depth`arguments like the`stochastic`decorator.**Direct**The same potential could be created directly as follows:

def psi_i_logp(x_lo = x[i], x_hi = x[i+1]): return -(xlo - xhi)**2 psi_i = pymc.Potential(logp = psi_i_logp, name = 'psi_i', parents = {'xlo': x[i], 'xhi': x[i+1]}, doc = 'A pair potential', verbose = 0, cache_depth = 2)

The function `dag` (or `graph`) in `pymc.graph` draws graphical
representations of `Model` (Chapter *Fitting Models*) instances using
GraphViz via the Python package PyDot. See [Lauritzen1990] and [Jordan2004]
for more discussion of useful information that can be read off of graphical
models [4].

The symbol for stochastic variables is an ellipse. Parent-child relationships
are indicated by arrows. These arrows point from parent to child and are
labeled with the names assigned to the parents by the children. PyMC’s symbol
for deterministic variables is a downward-pointing triangle. A graphical
representation of model (?) is shown in *Directed acyclic graph of the relationships in the coal mining disaster model example.*. Note that
\(D\) is shaded because it is flagged as data.

The symbol for factor potentials is a rectangle, as in the following model.
Factor potentials are usually associated with *undirected* grahical models. In
undirected representations, each parent of a potential is connected to every
other parent by an undirected edge. The undirected representation of the model
pictured above is much more compact: Directed or mixed graphical models can be
represented in an undirected form by ‘moralizing’, which is done by the
function `pymc.graph.moral_graph`.

This section gives an overview of how PyMC computes log-probabilities. This is advanced information that is not required in order to use PyMC.

The `logp` attributes of stochastic variables and potentials and the
`value` attributes of deterministic variables are wrappers for instances of
class `LazyFunction`. Lazy functions are wrappers for ordinary Python
functions. A lazy function `L` could be created from a function `fun` as
follows:

```
L = pymc.LazyFunction(fun, arguments)
```

The argument `arguments` is a dictionary container; `fun` must accept
keyword arguments only. When `L`‘s `get()` method is called, the return
value is the same as the call

```
fun(**arguments.value)
```

Note that no arguments need to be passed to `L.get`; lazy functions memorize
their arguments.

Before calling `fun`, `L` will check the values of `arguments.variables`
against an internal cache. This comparison is done *by reference*, not by
value, and this is part of the reason why stochastic variables’ values cannot
be updated in-place. If `arguments.variables`‘ values match a frame of the
cache, the corresponding output value is returned and `fun` is not called. If
a call to `fun` is needed, `arguments.variables`‘ values and the return
value replace the oldest frame in the cache. The depth of the cache can be set
using the optional init argument `cache_depth`, which defaults to 2.

Caching is helpful in MCMC, because variables’ log-probabilities and values tend to be queried multiple times for the same parental value configuration. The default cache depth of 2 turns out to be most useful in Metropolis-Hastings-type algorithms involving proposed values that may be rejected.

Lazy functions are implemented in C using Pyrex, a language for writing Python extensions.

Footnotes

[1] | Note that the random method does not provide a Gibbs sample unless |

the variable has no children.

[2] | Note that deterministic variables have no observed flag. If a |

deterministic variable’s value were known, its parents would be restricted to
the inverse image of that value under the deterministic variable’s evaluation
function. This usage would be extremely difficult to support in general, but it
can be implemented for particular applications at the `StepMethod` level.

[3] | Nodes whose parents are containers make private shallow copies of those |

containers. This is done for technical reasons rather than to protect users from accidental misuse.

[4] | Note that these authors do not consider deterministic variables. |