Help on class understanding in pymc code

[Robert]

Hi,

I follow code example at link:

https://users.obs.carnegiescience.edu/cburns/ipynbs/PyMC.html


There is the following code line:

sampler = pymc.MCMC([alpha,betax,betay,eps,model,tau,z_obs,x_true,y_true])


I want to know the detail of pymc.MCMC, then I get help content of it with:

/////////////
help(pymc.MCMC)
Help on class MCMC in module pymc.MCMC:

class MCMC(pymc.Model.Sampler)
 |  This class fits probability models using Markov Chain Monte Carlo. Each stochastic variable
 |  is assigned a StepMethod object, which makes it take a single MCMC step conditional on the
 |  rest of the model. These step methods are called in turn.
 |  
 |    >>> A = MCMC(input, db, verbose=0)
 |  
\\\\\\\\\\\\\\\\\\


help('pymc.Model.Sampler')
no Python documentation found for 'pymc.Model.Sampler'


help('pymc.Model')
Help on class Model in pymc:

pymc.Model = class Model(pymc.Container.ObjectContainer)
 |  The base class for all objects that fit probability models. Model is initialized with:
 |  
 |    >>> A = Model(input, verbose=0)
 |  
 |    :Parameters:
 |      - input : module, list, tuple, dictionary, set, object or nothing.
 |          Model definition, in terms of Stochastics, Deterministics, Potentials and Containers.
 |          If nothing, all nodes are collected from the base namespace.
 |  
 |  Attributes:
 |    - deterministics
 |    - stochastics (with observed=False)
 |    - data (stochastic variables with observed=True)
 |    - variables
 |    - potentials
 |    - containers
 |    - nodes
 |    - all_objects
 |    - status: Not useful for the Model base class, but may be used by subclasses.
 |  
 |  The following attributes only exist after the appropriate method is called:
 |    - moral_neighbors: The edges of the moralized graph. A dictionary, keyed by stochastic variable,
 |      whose values are sets of stochastic variables. Edges exist between the key variable and all variables
 |      in the value. Created by method _moralize.
 |    - extended_children: The extended children of self's stochastic variables. See the docstring of
 |      extend_children. This is a dictionary keyed by stochastic variable.
 |    - generations: A list of sets of stochastic variables. The members of each element only have parents in
 |      previous elements. Created by method find_generations.
 |  
 |  Methods:
 |     - sample_model_likelihood(iter): Generate and return iter samples of p(data and potentials|model).
 |       Can be used to generate Bayes' factors.
 |  
 |  :SeeAlso: Sampler, MAP, NormalApproximation, weight, Container, graph.
 |  
 |  Method resolution order:
 |      Model
 |      pymc.Container.ObjectContainer
 |      pymc.six.NewBase
 |      pymc.Node.ContainerBase
 |      __builtin__.object
 |  
 |  Methods defined here:
 |  
 |  __init__(self, input=None, name=None, verbose=-1)
 |      Initialize a Model instance.
 |      
 |      :Parameters:
 |        - input : module, list, tuple, dictionary, set, object or nothing.
 |            Model definition, in terms of Stochastics, Deterministics, Potentials and Containers.
 |            If nothing, all nodes are collected from the base namespace.
 |  
 |  draw_from_prior(self)
 |      Sets all variables to random values drawn from joint 'prior', meaning contributions
 |      of data and potentials to the joint distribution are not considered.
 |  
 |  get_node(self, node_name)
 |      Retrieve node with passed name
 |  
 |  seed(self)
 |      Seed new initial values for the stochastics.
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |  
 |  generations
 |  
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |  
 |  __slotnames__ = []
 |  
 |  register = False
 |  
 |  ----------------------------------------------------------------------
 |  Methods inherited from pymc.Container.ObjectContainer:
 |  
 |  replace(self, item, new_container, key)
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from pymc.Container.ObjectContainer:
 |  
 |  value
 |      A copy of self, with all variables replaced by their values.
 |  
 |  ----------------------------------------------------------------------
 |  Methods inherited from pymc.Node.ContainerBase:
 |  
 |  assimilate(self, new_container)
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from pymc.Node.ContainerBase:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)
 |  
 |  logp
 |      The summed log-probability of all stochastic variables (data
 |      or otherwise) and factor potentials in self.
 |  
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from pymc.Node.ContainerBase:
 |  
 |  change_methods = []
 |  
 |  containing_classes = []
---------


Now, I have puzzles on the class constructor input parameter:
[alpha,betax,betay,eps,model,tau,z_obs,x_true,y_true]

1. 'class MCMC(pymc.Model.Sampler)' says its inheritance is from 
'pymc.Model.Sampler'

2. When I try to get help on 'pymc.Model.Sampler', it says:
   'no Python documentation found for 'pymc.Model.Sampler'

3. When I continue to catch help on 'pymc.Model.Sampler', I don't see
content mentions 'Sampler'. This complete help message is shown above.

So, what is 'pymc.Model.Sampler'?


BTW, I use Enthought Canopy, Python 2.7.

Thanks,