Help on class understanding in pymc code

Sun Dec 13 19:40:47 EST 2015

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,