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data_sim_table#
View page sourceSimulated Measurements and Adjusted Standard Deviations#
Discussion#
The data_sim
table is created during a
simulate_command .
It contains number_simulate
sets of measurements where each set
has one value for each entry in the data_subset_table .
data_sim_id#
This column has type integer
and is the primary key for this table.
Its initial value is zero, and it increments by one for each row.
Given the model_variables as specified by
truth_var_table ,
the measurement uncertainty is independent for each row
and has standard deviation meas_std .
simulate_index#
The column has type integer
. It specifies the index
for this simulated measurement set. This index starts at zero,
repeats as the same for the entire subset of data_id values,
and then increments by one between measurement sets.
The final (maximum) value for simulate_index is
number_simulate minus one.
data_subset_id#
This column has type integer
and is the primary key
for the data_subset_table .
This identifies which data_id
each row of the data_sim table corresponds to.
If n_subset is the number of rows in the data_subset table,
data_sim_id = simulate_index * n_subset + data_subset_id
for simulate_index equal zero to number_simulate -1
and data_subset_id equal zero to n_subset
-1 .
data_sim_value#
This column has type real
and is
the simulated measurement value that for the specified row of the data table;
see z in the method below.
If the density for this data_id is censored (not censored)
data_sim_value has value max
( z , 0) , ( z ).
Method#
data_id#
We use data_id to denote the data_id corresponding to the data_subset_id corresponding to this data_sim_id .
y#
We use \(y\) to denote the data table meas_value corresponding to this data_id .
Capital Delta#
We use \(\Delta\) to denote the minimum cv standard deviation corresponding to the data table and this data_id .
d#
We use \(d\) to denote the density_id corresponding to the data table and this data_id .
eta#
We use \(\eta\) to denote the eta corresponding to the data table and this data_id .
A#
We use \(A\) denote the average integrand corresponding to the truth_var table value for the model variables, the values in the data table, and this data_id .
Capital E#
We use \(E\) for the average noise effect corresponding to the truth_var table value for the model variables, the values in the data table, and this data_id .
sigma#
We use \(\sigma\) to denote the adjusted standard deviation sigma corresponding to the data table and this data_id . Note that \(\sigma\) does not depend on simulated noise \(e\) defined below (because it is defined using \(y\) instead of \(z\)).
delta#
We use \(\delta\) to denote the transformed standard deviation delta corresponding to the truth_var table value for the model variables, the values in the data table, and this data_id . Note that \(\delta\) does not depend on simulated noise \(e\) defined below.
e#
We use \(e\) to denote the noise simulated with mean zero,
standard deviation \(\delta\), and density corresponding to
this data_id without log qualification.
For example, if the data density for this data_id is
log_gaussian
, the \(e\) is simulate using a Gaussian
distribution.
z#
We use \(z\) to denote the simulated data value data_sim_value corresponding to this data_sim_id . It the density is Linear ,
It the density is Log Scaled ,
Example#
See the user_data_sim.py and simulate_command.py examples / tests.