pymc.model.core.BaseModel#

class pymc.model.core.BaseModel(*args, **kwargs)[source]#

Functionality shared by mutable and frozen models.

Abstract: instantiate Model (mutable), or create a FrozenModel with pymc.model.transform.optimization.freeze_model().

Methods

`BaseModel.__init__`([name, coords, ...])
`BaseModel.check_start_vals`(start, **kwargs)	Check that the logp is defined and finite at the starting point.
`BaseModel.compile_d2logp`([vars, jacobian, ...])	Compiled log probability density hessian function.
`BaseModel.compile_dlogp`([vars, jacobian])	Compiled log probability density gradient function.
`BaseModel.compile_fn`(outs, *[, inputs, ...])	Compiles a PyTensor function.
`BaseModel.compile_logp`([vars, jacobian, sum])	Compiled log probability density function.
`BaseModel.copy`()	Clone the model.
`BaseModel.d2logp`([vars, jacobian, negate_output])	Hessian of the models log-probability w.r.t.
`BaseModel.debug`([point, fn, verbose])	Debug model function at point.
`BaseModel.dlogp`([vars, jacobian])	Gradient of the models log-probability w.r.t.
`BaseModel.eval_rv_shapes`()	Evaluate shapes of untransformed AND transformed free variables.
`BaseModel.get_context`([error_if_none, ...])
`BaseModel.initial_point`([random_seed])	Compute the initial point of the model.
`BaseModel.logp`([vars, jacobian, sum])	Elemwise log-probability of the model.
`BaseModel.logp_dlogp_function`([grad_vars, ...])	Compile a PyTensor function that computes logp and gradient.
`BaseModel.name_for`(name)	Check if name has prefix and adds if needed.
`BaseModel.name_of`(name)	Check if name has prefix and deletes if needed.
`BaseModel.point_logps`([point, round_vals])	Compute the log probability of point for all random variables in the model.
`BaseModel.profile`(outs, *[, n, point, ...])	Compile and profile a PyTensor function which returns `outs` and takes values of model vars as a dict as an argument.
`BaseModel.replace_rvs_by_values`(graphs, **kwargs)	Clone and replace random variables in graphs with their value variables.
`BaseModel.set_data`(name, values[, coords])	Change the values of a data variable in the model.
`BaseModel.set_dim`(name, new_length[, ...])	Update a mutable dimension.
`BaseModel.shape_from_dims`(dims)
`BaseModel.table`(*[, split_groups, ...])	Create a rich table summarizing the model's variables and their expressions.
`BaseModel.to_graphviz`(*[, var_names, ...])	Produce a graphviz Digraph from a PyMC model.

Attributes

`basic_RVs`	List of random variables the model is defined in terms of.
`continuous_value_vars`	All the continuous value variables in the model.
`coords`	Coordinate values for model dimensions.
`datalogp`	PyTensor scalar of log-probability of the observed variables and potential terms.
`dim_lengths`	The symbolic lengths of dimensions in the model.
`discrete_value_vars`	All the discrete value variables in the model.
`isroot`
`observedlogp`	PyTensor scalar of log-probability of the observed variables.
`parent`
`potentiallogp`	PyTensor scalar of log-probability of the Potential terms.
`prefix`
`root`
`unobserved_RVs`	List of all random variables, including deterministic ones.
`unobserved_value_vars`	List of all random variables (including untransformed projections), as well as deterministics used as inputs and outputs of the model's log-likelihood graph.
`value_vars`	List of unobserved random variables used as inputs to the model's log-likelihood (which excludes deterministics).
`varlogp`	PyTensor scalar of log-probability of the unobserved random variables (excluding deterministic).
`varlogp_nojac`	PyTensor scalar of log-probability of the unobserved random variables (excluding deterministic) without jacobian term.