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, -- https://mail.python.org/mailman/listinfo/python-list
