# 5. Fitting Models¶

PyMC provides three objects that fit models:

`MCMC`

, which coordinates Markov chain Monte Carlo algorithms. The actual work of updating stochastic variables conditional on the rest of the model is done by`StepMethod`

objects, which are described in this chapter.`MAP`

, which computes maximum*a posteriori*estimates.`NormApprox`

, which computes the ‘normal approximation’ [Gelman2004]: the joint distribution of all stochastic variables in a model is approximated as normal using local information at the maximum*a posteriori*estimate.

All three objects are subclasses of `Model`

, which is PyMC’s base class for
fitting methods. `MCMC`

and `NormApprox`

, both of which can produce samples
from the posterior, are subclasses of `Sampler`

, which is PyMC’s base class
for Monte Carlo fitting methods. `Sampler`

provides a generic sampling loop
method and database support for storing large sets of joint samples. These base
classes implement some basic methods that are inherited by the three
implemented fitting methods, so they are documented at the end of this section.

## 5.1. Creating models¶

The first argument to any fitting method’s `__init__`

method, including that
of `MCMC`

, is called `input`

. The `input`

argument can be just about
anything; once you have defined the nodes that make up your model, you
shouldn’t even have to think about how to wrap them in a `Model`

instance.
Some examples of model instantiation using nodes `a`

, `b`

and `c`

follow:

`M = MCMC(set([a,b,c]))`

This will create a`MCMC`

model with \(a\), \(b\), and \(c\) as components, each of which will be exposed as attributes of`M`

(e.g.`M.a`

).`M = MCMC({`a': a, `d': [b,c]})`

In this case, \(M\) will expose \(a\) and \(d\) as attributes:`M.a`

will be \(a\), and`M.d`

will be`[b,c]`

.`M = MAP([[a,b],c])`

This will create a`MAP`

model with \(a\) and \(b\) as a`Container`

object and \(c\) exposed on its own.If file

`MyModule`

contains the definitions of`a`

,`b`

and`c`

:import MyModule M = Model(MyModule)

In this case, \(M\) will expose \(a\), \(b\) and \(c\) as attributes.

Using a ‘model factory’ function:

def make_model(x): a = pymc.Exponential('a', beta=x, value=0.5) @pymc.deterministic def b(a=a): return 100-a @pymc.stochastic def c(value=.5, a=a, b=b): return (value-a)**2/b return locals() M = pymc.MCMC(make_model(3))

In this case, \(M\) will also expose \(a\), \(b\) and \(c\) as attributes.

## 5.2. The Model class¶

This class serves as a container for probability models and as a base class for
the classes responsible for model fitting, such as `MCMC`

.

`Model`

‘s init method takes the following arguments:

`input`

:- Some collection of PyMC nodes defining a probability model. These may be
stored in a list, set, tuple, dictionary, array, module, or any object with
a
`__dict__`

attribute. `verbose`

(optional):- An integer controlling the verbosity of the model’s output.

Models’ useful methods are:

`draw_from_prior()`

:- Sets all stochastic variables’ values to new random values, which would be a
sample from the joint distribution if all data and
`Potential`

instances’ log-probability functions returned zero. If any stochastic variables lack a`random()`

method, PyMC will raise an exception. `seed()`

:- Same as
`draw_from_prior`

, but only`stochastics`

whose`rseed`

attribute is not`None`

are changed.

As introduced in the previous chapter, the helper function `graph.dag`

produces graphical representations of models (see [Jordan2004]).

Models have the following important attributes:

`variables`

`nodes`

`stochastics`

`potentials`

`deterministics`

`observed_stochastics`

`containers`

`value`

`logp`

In addition, models expose each node they contain as an attribute. For
instance, if model `M`

were produced from model ((?)) `M.s`

would return the switchpoint variable.

Though one may instantiate `Model`

objects directly, most users should pefer
to instantiate the `Model`

subclass that they will be using to fit their model.
These are each described below.

## 5.3. Maximum a posteriori estimates¶

The `MAP`

class sets all stochastic variables to their maximum *a posteriori*
values using functions in SciPy’s `optimize`

package; hence, SciPy must be
installed to use it. `MAP`

can only handle variables whose dtype is
`float`

, so it will not work, for example, on model ((?)). To
fit the model in `examples/gelman_bioassay.py`

using `MAP`

, do the
following:

```
>>> from pymc.examples import gelman_bioassay
>>> M = pymc.MAP(gelman_bioassay)
>>> M.fit()
```

This call will cause \(M\) to fit the model using modified Powell optimization,
which does not require derivatives. The variables in `DisasterModel`

have now
been set to their maximum *a posteriori* values:

```
>>> M.alpha.value
array(0.8465892309923545)
>>> M.beta.value
array(7.7488499785334168)
```

In addition, the AIC and BIC of the model are now available:

```
>>> M.AIC
7.9648372671389458
>>> M.BIC
6.7374259893787265
```

`MAP`

has two useful methods:

`fit(method='fmin_powell', iterlim=1000, tol=.0001)`

:- The optimization method may be
`fmin`

,`fmin_l_bfgs_b`

,`fmin_ncg`

,`fmin_cg`

, or`fmin_powell`

. See the documentation of SciPy’s`optimize`

package for the details of these methods. The`tol`

and`iterlim`

parameters are passed to the optimization function under the appropriate names. `revert_to_max()`

:- If the values of the constituent stochastic variables change after fitting,
this function will reset them to their maximum
*a posteriori*values.

If you’re going to use an optimization method that requires derivatives,
`MAP`

‘s `__init__`

method can take additional parameters `eps`

and
`diff_order`

. `diff_order`

, which must be an integer, specifies the order
of the numerical approximation (see the SciPy function `derivative`

). The
step size for numerical derivatives is controlled by `eps`

, which may be
either a single value or a dictionary of values whose keys are variables
(actual objects, not names).

The useful attributes of `MAP`

are:

`logp`

:- The joint log-probability of the model.
`logp_at_max`

:- The maximum joint log-probability of the model.
`AIC`

:- Akaike’s information criterion for this model ([Akaike1973],[Burnham2002]_).
`BIC`

:- The Bayesian information criterion for this model [Schwarz1978].

One use of the `MAP`

class is finding reasonable initial states for MCMC
chains. Note that multiple `Model`

subclasses can handle the same collection
of nodes.

## 5.4. Normal approximations¶

The `NormApprox`

class extends the `MAP`

class by approximating the
posterior covariance of the model using the Fisher information matrix, or the
Hessian of the joint log probability at the maximum. To fit the model in
`examples/gelman_bioassay.py`

using `NormApprox`

, do:

```
>>> N = pymc.NormApprox(gelman_bioassay)
>>> N.fit()
```

The approximate joint posterior mean and covariance of the variables are
available via the attributes `mu`

and `C`

:

```
>>> N.mu[N.alpha]
array([ 0.84658923])
>>> N.mu[N.alpha, N.beta]
array([ 0.84658923, 7.74884998])
>>> N.C[N.alpha]
matrix([[ 1.03854093]])
>>> N.C[N.alpha, N.beta]
matrix([[ 1.03854093, 3.54601911],
[ 3.54601911, 23.74406919]])
```

As with `MAP`

, the variables have been set to their maximum *a posteriori*
values (which are also in the `mu`

attribute) and the AIC and BIC of the
model are available.

In addition, it’s now possible to generate samples from the posterior as with
`MCMC`

:

```
>>> N.sample(100)
>>> N.trace('alpha')[::10]
array([-0.85001278, 1.58982854, 1.0388088 , 0.07626688, 1.15359581,
-0.25211939, 1.39264616, 0.22551586, 2.69729987, 1.21722872])
>>> N.trace('beta')[::10]
array([ 2.50203663, 14.73815047, 11.32166303, 0.43115426,
10.1182532 , 7.4063525 , 11.58584317, 8.99331152,
11.04720439, 9.5084239 ])
```

Any of the database backends can be used (chapter Saving and managing sampling results).

In addition to the methods and attributes of `MAP`

, `NormApprox`

provides
the following methods:

`sample(iter)`

:- Samples from the approximate posterior distribution are drawn and stored.
`isample(iter)`

:- An ‘interactive’ version of
`sample()`

: sampling can be paused, returning control to the user. `draw`

:- Sets all variables to random values drawn from the approximate posterior.

It provides the following additional attributes:

`mu`

:- A special dictionary-like object that can be keyed with multiple variables.
`N.mu[p1, p2, p3]`

would return the approximate posterior mean values of stochastic variables`p1`

,`p2`

and`p3`

, raveled and concatenated to form a vector. `C`

:- Another special dictionary-like object.
`N.C[p1, p2, p3]`

would return the approximate posterior covariance matrix of stochastic variables`p1`

,`p2`

and`p3`

. As with`mu`

, these variables’ values are raveled and concatenated before their covariance matrix is constructed.

## 5.5. Markov chain Monte Carlo: the MCMC class¶

The `MCMC`

class implements PyMC’s core business: producing ‘traces’ for a
model’s variables which, with careful thinning, can be considered independent
joint samples from the posterior. See Tutorial for an example of
basic usage.

`MCMC`

‘s primary job is to create and coordinate a collection of ‘step
methods’, each of which is responsible for updating one or more variables. The
available step methods are described below. Instructions on how to create your
own step method are available in Extending PyMC.

`MCMC`

provides the following useful methods:

`sample(iter, burn, thin, tune_interval, tune_throughout, save_interval, ...)`

:- Runs the MCMC algorithm and produces the traces. The
`iter`

argument controls the total number of MCMC iterations. No tallying will be done during the first`burn`

iterations; these samples will be forgotten. After this burn-in period, tallying will be done each`thin`

iterations. Tuning will be done each`tune_interval`

iterations. If`tune_throughout=False`

, no more tuning will be done after the burnin period. The model state will be saved every`save_interval`

iterations, if given. `isample(iter, burn, thin, tune_interval, tune_throughout, save_interval, ...)`

:- An interactive version of
`sample`

. The sampling loop may be paused at any time, returning control to the user. `use_step_method(method, *args, **kwargs)`

:- Creates an instance of step method class
`method`

to handle some stochastic variables. The extra arguments are passed to the`init`

method of`method`

. Assigning a step method to a variable manually will prevent the`MCMC`

instance from automatically assigning one. However, you may handle a variable with multiple step methods. `goodness()`

:- Calculates goodness-of-fit (GOF) statistics according to [Brooks2000].
`save_state()`

:- Saves the current state of the sampler, including all stochastics, to the
database. This allows the sampler to be reconstituted at a later time to
resume sampling. This is not supported yet for the
`sqlite`

backend. `restore_state()`

:- Restores the sampler to the state stored in the database.
`stats()`

:- Generate summary statistics for all nodes in the model.
`remember(trace_index)`

:- Set all variables’ values from frame
`trace_index`

in the database.

MCMC samplers’ step methods can be accessed via the `step_method_dict`

attribute. `M.step_method_dict[x]`

returns a list of the step methods `M`

will use to handle the stochastic variable `x`

.

After sampling, the information tallied by `M`

can be queried via
`M.db.trace_names`

. In addition to the values of variables, tuning
information for adaptive step methods is generally tallied. These ‘traces’ can
be plotted to verify that tuning has in fact terminated.

You can produce ‘traces’ for arbitrary functions with zero arguments as well.
If you issue the command `M._funs_to_tally['trace_name'] = f`

before sampling
begins, then each time the model variables’ values are tallied, `f`

will be
called with no arguments, and the return value will be tallied. After sampling
ends you can retrieve the trace as `M.trace[’trace_name’]`

.

## 5.6. The Sampler class¶

`MCMC`

is a subclass of a more general class called `Sampler`

. Samplers fit
models with Monte Carlo fitting methods, which characterize the posterior
distribution by approximate samples from it. They are initialized as follows:
```
Sampler(input=None, db='ram', name='Sampler', reinit_model=True,
calc_deviance=False, verbose=0)
```

. The `input`

argument is a module, list,
tuple, dictionary, set, or object that contains all elements of the model, the
`db`

argument indicates which database backend should be used to store the
samples (see chapter Saving and managing sampling results), `reinit_model`

is a boolean flag
that indicates whether the model should be re-initialised before running, and
`calc_deviance`

is a boolean flag indicating whether deviance should be
calculated for the model at each iteration. Samplers have the following
important methods:

`sample(iter, length, verbose, ...)`

:- Samples from the joint distribution. The
`iter`

argument controls how many times the sampling loop will be run, and the`length`

argument controls the initial size of the database that will be used to store the samples. `isample(iter, length, verbose, ...)`

:- The same as
`sample`

, but the sampling is done interactively: you can pause sampling at any point and be returned to the Python prompt to inspect progress and adjust fitting parameters. While sampling is paused, the following methods are useful: `icontinue()`

:- Continue interactive sampling.
`halt()`

:- Truncate the database and clean up.
`tally()`

:- Write all variables’ current values to the database. The actual write operation depends on the specified database backend.
`save_state()`

:- Saves the current state of the sampler, including all stochastics, to the
database. This allows the sampler to be reconstituted at a later time to
resume sampling. This is not supported yet for the
`sqlite`

backend. `restore_state()`

:- Restores the sampler to the state stored in the database.
`stats()`

:- Generate summary statistics for all nodes in the model.
`remember(trace_index)`

:- Set all variables’ values from frame
`trace_index`

in the database. Note that the`trace_index`

is different from the current iteration, since not all samples are necessarily saved due to burning and thinning.

In addition, the sampler attribute `deviance`

is a deterministic variable
valued as the model’s deviance at its current state.

## 5.7. Step methods¶

Step method objects handle individual stochastic variables, or sometimes groups
of them. They are responsible for making the variables they handle take single
MCMC steps conditional on the rest of the model. Each subclass of
`StepMethod`

implements a method called `step()`

, which is called by
`MCMC`

. Step methods with adaptive tuning parameters can optionally implement
a method called `tune()`

, which causes them to assess performance (based on
the acceptance rates of proposed values for the variable) so far and adjust.

The major subclasses of `StepMethod`

are `Metropolis`

,
`AdaptiveMetropolis`

and `Slicer`

. PyMC provides several flavors of the
basic Metropolis steps. There are Gibbs sampling (`Gibbs`

) steps, but they are not
ready for use as of the current release, but since it is feasible to write Gibbs step
methods for particular applications, the `Gibbs`

base class will be documented here.

### 5.7.1. Metropolis step methods¶

`Metropolis`

and subclasses implement Metropolis-Hastings steps. To tell an
`MCMC`

object \(M\) to handle a variable `x`

with a Metropolis step
method, you might do the following:

```
M.use_step_method(pymc.Metropolis, x, proposal_sd=1., proposal_distribution='Normal')
```

`Metropolis`

itself handles float-valued variables, and subclasses
`DiscreteMetropolis`

and `BinaryMetropolis`

handle integer- and
boolean-valued variables, respectively. Subclasses of `Metropolis`

must
implement the following methods:

`propose()`

:- Sets the values of the variables handled by the Metropolis step method to proposed values.
`reject()`

:- If the Metropolis-Hastings acceptance test fails, this method is called to
reset the values of the variables to their values before
`propose()`

was called.

Note that there is no `accept()`

method; if a proposal is accepted, the
variables’ values are simply left alone. Subclasses that use proposal
distributions other than symmetric random-walk may specify the ‘Hastings
factor’ by changing the `hastings_factor`

method. See Extending PyMC
for an example.

`Metropolis`

‘ `__init__`

method takes the following arguments:

`stochastic`

:- The variable to handle.
`proposal_sd`

:- A float or array of floats. This sets the proposal standard deviation if the proposal distribution is normal.
`scale`

:A float, defaulting to 1. If the

`scale`

argument is provided but not`proposal_sd`

,`proposal_sd`

is computed as follows:if all(self.stochastic.value != 0.): self.proposal_sd = ones(shape(self.stochastic.value)) * \ abs(self.stochastic.value) * scale else: self.proposal_sd = ones(shape(self.stochastic.value)) * scale

`proposal_distribution`

:- A string indicating which distribution should be used for proposals. Current
options are
`'Normal'`

and`'Prior'`

. If`proposal_distribution=None`

, the proposal distribution is chosen automatically. It is set to`'Prior'`

if the variable has no children and has a random method, and to`'Normal'`

otherwise. `verbose`

:- An integer. By convention 0 indicates no output, 1 shows a progress bar only, 2 provides basic feedback about the current MCMC run, while 3 and 4 provide low and high debugging verbosity, respectively.

Alhough the `proposal_sd`

attribute is fixed at creation, Metropolis step
methods adjust their initial proposal standard deviations using an attribute
called `adaptive_scale_factor`

. When `tune()`

is called, the acceptance
ratio of the step method is examined, and this scale factor is updated
accordingly. If the proposal distribution is normal, proposals will have
standard deviation `self.proposal_sd * self.adaptive_scale_factor`

.

By default, tuning will continue throughout the sampling loop, even after the
burnin period is over. This can be changed via the `tune_throughout`

argument
to `MCMC.sample`

. If an adaptive step method’s `tally`

flag is set (the
default for `Metropolis`

), a trace of its tuning parameters will be kept. If
you allow tuning to continue throughout the sampling loop, it is important to
verify that the ‘Diminishing Tuning’ condition of [Roberts2007] is satisfied:
the amount of tuning should decrease to zero, or tuning should become very
infrequent.

If a Metropolis step method handles an array-valued variable, it proposes all
elements independently but simultaneously. That is, it decides whether to
accept or reject all elements together but it does not attempt to take the
posterior correlation between elements into account. The `AdaptiveMetropolis`

class (see below), on the other hand, does make correlated proposals.

### 5.7.2. The AdaptiveMetropolis class¶

The `AdaptativeMetropolis`

(AM) step method works like a regular Metropolis
step method, with the exception that its variables are block-updated using a
multivariate jump distribution whose covariance is tuned during sampling.
Although the chain is non-Markovian, it has correct ergodic properties (see
[Haario2001]).

To tell an `MCMC`

object \(M\) to handle variables `x`

, `y`

and \(z\) with an `AdaptiveMetropolis`

instance, you might do the
following:

```
M.use_step_method(pymc.AdaptiveMetropolis, [x,y,z], \
scales={'x':1, 'y':2, 'z':.5}, delay=10000)
```

`AdaptativeMetropolis`

‘s init method takes the following arguments:

`stochastics`

:- The stochastic variables to handle. These will be updated jointly.
`cov`

(optional):- An initial covariance matrix. Defaults to the identity matrix, adjusted
according to the
`scales`

argument. `delay`

(optional):- The number of iterations to delay before computing the empirical covariance matrix.
`scales`

(optional):- The initial covariance matrix will be diagonal, and its diagonal elements
will be set to
`scales`

times the stochastics’ values, squared. `interval`

(optional):- The number of iterations between updates of the covariance matrix. Defaults to 1000.
`greedy`

(optional):- If
`True`

, only accepted jumps will be counted toward the delay before the covariance is first computed. Defaults to`True`

. `verbose`

:- An integer from 0 to 4 controlling the verbosity of the step method’s printed output.
`shrink_if_necessary`

(optional):- Whether the proposal covariance should be shrunk if the acceptance rate becomes extremely small.

In this algorithm, jumps are proposed from a multivariate normal distribution
with covariance matrix \(\Sigma\). The algorithm first iterates until
`delay`

samples have been drawn (if `greedy`

is true, until `delay`

jumps
have been accepted). At this point, \(\Sigma\) is given the value of the
empirical covariance of the trace so far and sampling resumes. The covariance
is then updated each `interval`

iterations throughout the entire sampling run
[1]. It is this constant adaptation of the proposal distribution that makes
the chain non-Markovian.

### 5.7.3. The DiscreteMetropolis class¶

This class is just like `Metropolis`

, but specialized to handle
`Stochastic`

instances with dtype `int`

. The jump proposal distribution can
either be `'Normal'`

, `'Prior'`

or `'Poisson'`

(the default). In the
normal case, the proposed value is drawn from a normal distribution centered at
the current value and then rounded to the nearest integer.

### 5.7.4. The BinaryMetropolis class¶

This class is specialized to handle `Stochastic`

instances with dtype
`bool`

.

For array-valued variables, `BinaryMetropolis`

can be set to propose from the
prior by passing in `dist="Prior"`

. Otherwise, the argument `p_jump`

of the
init method specifies how probable a change is. Like `Metropolis`

‘ attribute
`proposal_sd`

, `p_jump`

is tuned throughout the sampling loop via
`adaptive_scale_factor`

.

For scalar-valued variables, `BinaryMetropolis`

behaves like a Gibbs sampler,
since this requires no additional expense. The `p_jump`

and
`adaptive_scale_factor`

parameters are not used in this case.

### 5.7.5. The Slicer class¶

The `Slicer`

class implements Slice sampling ([Neal2003]). To tell an
`MCMC`

object \(M\) to handle a variable `x`

with a Slicer step
method, you might do the following:

```
M.use_step_method(pymc.Slicer, x, w=10, m=10000, doubling=True)
```

`Slicer`

‘s init method takes the following arguments:

`stochastics`

:- The stochastic variables to handle. These will be updated jointly.
`w`

(optional):- The initial width of the horizontal slice (Defaults to 1). This will be updated via either stepping-out or doubling procedures.
`m`

(optional):- The multiplier defining the maximum slice size as \(mw\) (Defaults to 1000).
`tune`

(optional):- A flag indicating whether to tune the initial slice width (Defaults to
`True`

). `doubling`

(optional):- A flag for using doubling procedure instead of stepping out (Defaults to
`False`

) `tally`

(optional):- Flag for recording values for trace (Defaults to
`True`

). `verbose`

:- An integer from -1 to 4 controlling the verbosity of the step method’s printed output (Defaults to -1).

The ***slice sampler*** generates posterior samples by alternately drawing “slices” from
the vertical (y) and horizontal (x) planes of a distribution. It first samples from the
conditional distribution for `y`

given some current value of `x`

, which is
uniform over the \((0, f (x))\). Conditional on this value for `y`

, it then
samples `x`

, which is uniform on \(S = {x : y < f (x)}\); that is the “slice”
defined by the `y`

value. Hence, this algorithm automatically adapts its to the
local characteristics of the posterior.

The steps required to perform a single iteration of the slice sampler to update the current value of \(x_i\) is as follows:

- Sample
`y`

uniformly on \((0,f(x_i))\). - Use this value
`y`

to define a horizontal*slice*\(S = \{x : y < f (x)\}\). - Establish an interval, \(I = (x_{a}, x_{b})\), around \(x_i\) that contains most of the slice.
- Sample \(x_{i+1}\) from the region of the slice overlaping
`I`

.

Hence, slice sampling employs an *auxilliary variable* (`y`

) that is not retained at the
end of the iteration. Note that in practice one may operate on the log scale such that
\(g(x) = \log(f (x))\) to avoid floating-point underflow. In this case, the auxiliary
variable becomes \(z = log(y) = g(x_i) − e\), where \(e \sim \text{Exp}(1)\),
resulting in the slice \(S = \{x : z < g(x)\}\).

There are many ways of establishing and sampling from the interval `I`

, with the only
restriction being that the resulting Markov chain leaves \(f(x)\) invariant. The
objective is to include as much of the slice as possible, so that the potential step
size can be large, but not (much) larger than the slice, so that the sampling of
invalid points is minimized. Ideally, we would like it to be the slice itself, but it
may not always be feasible to determine (and certainly not automatically).

One method for determining a sampling interval for \(x_{i+1}\) involves specifying an
initial “guess” at the slice width `w`

, and iteratively moving the endpoints out
(growing the interval) until either (1) the interval reaches a maximum pre-specified
width or (2) `y`

is less than the \(f(x)\) evaluated both at the left and the
right interval endpoints. This is the *stepping out* method. The efficiency of
stepping out depends largely on the ability to pick a reasonable interval w from
which to sample. Otherwise, the *doubling* procedure may be preferable, as it can be
expanded faster. It simply doubles the size of the interval until both endpoints
are outside the slice.

## 5.8. Gibbs step methods¶

Gibbs step methods handle conjugate submodels. These models usually have two components: the `parent’ and the `children’. For example, a gamma-distributed variable serving as the precision of several normally-distributed variables is a conjugate submodel; the gamma variable is the parent and the normal variables are the children.

This section describes PyMC’s current scheme for Gibbs step methods, several of
which are in a semi-working state in the sandbox. It is meant to be as generic
as possible to minimize code duplication, but it is admittedly complicated.
Feel free to subclass `StepMethod`

directly when writing Gibbs step methods
if you prefer.

Gibbs step methods that subclass PyMC’s `Gibbs`

should define the following
class attributes:

`child_class`

:- The class of the children in the submodels the step method can handle.
`parent_class`

:- The class of the parent.
`parent_label`

:- The label the children would apply to the parent in a conjugate submodel.
In the gamma-normal example, this would be
`tau`

. `linear_OK`

:- A flag indicating whether the children can use linear combinations involving the parent as their actual parent without destroying the conjugacy.

A subclass of `Gibbs`

that defines these attributes only needs to implement a
`propose()`

method, which will be called by `Gibbs.step()`

. The resulting
step method will be able to handle both conjugate and ‘non-conjugate’ cases.
The conjugate case corresponds to an actual conjugate submodel. In the
nonconjugate case all the children are of the required class, but the parent is
not. In this case the parent’s value is proposed from the likelihood and
accepted based on its prior. The acceptance rate in the nonconjugate case will
be less than one.

The inherited class method `Gibbs.competence`

will determine the new step
method’s ability to handle a variable `x`

by checking whether:

- all
`x`

‘s children are of class`child_class`

, and either apply`parent_label`

to x directly or (if`linear_OK=True`

) to a`LinearCombination`

object (Building models), one of whose parents contains`x`

. `x`

is of class`parent_class`

If both conditions are met, `pymc.conjugate_Gibbs_competence`

will be
returned. If only the first is met, `pymc.nonconjugate_Gibbs_competence`

will
be returned.

### 5.8.1. Granularity of step methods: one-at-a-time vs. block updating¶

There is currently no way for a stochastic variable to compute individual terms of its log-probability; it is computed all together. This means that updating the elements of a array-valued variable individually would be inefficient, so all existing step methods update array-valued variables together, in a block update.

To update an array-valued variable’s elements individually, simply break it up into an array of scalar-valued variables. Instead of this:

```
A = pymc.Normal('A', value=zeros(100), mu=0., tau=1.)
```

do this:

```
A = [pymc.Normal('A_%i'%i, value=0., mu=0., tau=1.) for i in range(100)]
```

An individual step method will be assigned to each element of `A`

in the
latter case, and the elements will be updated individually. Note that `A`

can
be broken up into larger blocks if desired.

### 5.8.2. Automatic assignment of step methods¶

Every step method subclass (including user-defined ones) that does not require
any `__init__`

arguments other than the stochastic variable to be handled
adds itself to a list called `StepMethodRegistry`

in the PyMC namespace. If a
stochastic variable in an `MCMC`

object has not been explicitly assigned a
step method, each class in `StepMethodRegistry`

is allowed to examine the
variable.

To do so, each step method implements a class method called
`competence(stochastic)`

, whose only argument is a single stochastic
variable. These methods return values from 0 to 3; 0 meaning the step method
cannot safely handle the variable and 3 meaning it will most likely perform
well for variables like this. The `MCMC`

object assigns the step method that
returns the highest competence value to each of its stochastic variables.

Footnotes

[1] | The covariance is estimated recursively from the previous value and the last
`interval` samples, instead of computing it each time from the entire trace. |