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lines 6-134 of file: example/continue_cascade.py
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{xrst_begin_parent continue_cascade_xam}
{xrst_spell
  dage
  dtime
}

Example Continuing a Cascade
############################
For this example everything is constant in age and time.

Nodes
*****
The following is a diagram of the node tree for this example.
The :ref:`glossary@root_node` is n0,
the first :ref:`glossary@fit_goal_set` is {n3, n4, n2},
the second :ref:`glossary@fit_goal_set` is {n5, n6},
and the leaf nodes are {n3, n4, n5, n6}::

                     n0
             /-----/\-----\
          n1             (n2)
         /  \            /  \
     (n3)  (n4)      [n5]  [n6]

fit_goal_set
============
{xrst_literal
    # BEGIN fit_goal_set
    # END fit_goal_set
}
A cascade using the first fit_goal_set is started at node n0.
After that finishes, the cascade is continued from node n2
using the second fit_goal_set.

Parallel Processing
*******************
This example sets :ref:`option_all_table@max_number_cpu`
as an example of parallel processing.
The results for nodes n3 and n4 are computed in parallel
during the call to :ref:`cascade_root_node-name`.
The results for nodes n5 and n6 are computed in parallel
during the call to :ref:`continue_cascade-name`.
{xrst_literal
    # BEGIN option_all
    # END option_all
}
see :ref:`create_all_node_db@option_all`.

Rates
*****
The only non-zero dismod_at rate for this example is
:ref:`glossary@iota`.

Covariate
*********
There are no covariates for this example.

Simulated Data
**************

rate_true(rate, a, t, n, c)
===========================
For *rate* equal to iota,
this is the true value for *rate*
in node *n* at age *a*, time *t*, and covariate values *c*.
The values *a*, *t*. *c*, are not used by this function for this example.
{xrst_literal
    # BEGIN rate_true
    # END rate_true
}

y_i
===
The only simulated integrand for this example is :ref:`glossary@Sincidence`
which is a direct measurement of iota.
This data is simulated without any noise; i.e.,
the i-th measurement is simulated as
*y_i = rate_true('iota', None, None, n_i, None)* where *n_i* is the node.
The data is modeled as having noise even though there is no simulated noise.

n_i
===
Data is only simulated for the leaf nodes; i.e.,
each *n_i* is in the set { n3, n4, n5, n6 }.
Since the data does not have any nose, the data residuals are a measure
of how good the fit is for the nodes in the fit goal sets.

Parent Rate Smoothing
*********************
This is the iota smoothing used for the fit_node.
There are no :ref:`glossary@dage` or :ref:`glossary@dtime`
priors because there is only one age and one time point.

Value Prior
===========
The following is the value prior used for the root_node
{xrst_literal
    # BEGIN parent_value_prior
    # END parent_value_prior
}
The mean and standard deviation are only used for the root_node.
The :ref:`create_shift_db-name`
routine replaces them for other nodes.

Child Rate Smoothing
********************
This is the smoothing used for the
random effect for each child of the fit_node.
There are no dage or dtime
priors because there is only one age and one time point in this smoothing.

Value Prior
===========
The following is the value prior used for the children of the fit_node:
{xrst_literal
    # BEGIN child_value_prior
    # END child_value_prior
}

Checking The Fit
****************
The results of the fit are checked by check_cascade_node
using the :ref:`check_cascade_node@avgint_table`
that was created by the root_node_db routine.
The node_id for each row is replaced by the node_id for the
fit being checked.
routine uses these tables to check that fit against the truth.

{xrst_end continue_cascade_xam}