get_initial_param

Construct initial values for the parameters to be optimized over in <code>AIUQ</code>
method of <code>SAM</code> class.

internal

Uncertainty quantification and inverse estimation by probabilistic generative models from the beginning of the data analysis. An example is a Fourier basis method for inverse estimation in scattering analysis of microscopy videos. It does not require specifying a certain range of Fourier bases and it substantially reduces computational cost via the generalized Schur algorithm. See the reference: Mengyang Gu, Yue He, Xubo Liu and Yimin Luo (2023), <doi:10.48550/arXiv.2309.02468>.

Mengyang Gu

AIUQ

Ab Initio Uncertainty Quantification

Yue He

Xubo Liu

get_initial_param function

<dl><dt>model_name</dt>
<dd>fitted model, options from ('BM','OU','FBM','OU+FBM',
'user_defined'), with Brownian motion as the default model. See 'Details'.</dd>
<dt>sigma_0_2_ini</dt>
<dd>initial value for background noise, default is <code>NA</code></dd>
<dt>num_param</dt>
<dd>number of parameters need to be estimated in the model,
need to be non-NA value for <code>'user_defined'</code> model.</dd></dl>

Arguments

Author

Construct initial values for the parameters to be optimized over — get_initial_param

<dl>

<dt>model_name</dt>
<dd>fitted model, options from ('BM','OU','FBM','OU+FBM',
'user_defined'), with Brownian motion as the default model. See 'Details'.</dd>


<dt>sigma_0_2_ini</dt>
<dd>initial value for background noise, default is <code>NA</code></dd>


<dt>num_param</dt>
<dd>number of parameters need to be estimated in the model,
need to be non-NA value for <code>'user_defined'</code> model.</dd>

</dl>

get_initial_param: Construct initial values for the parameters to be optimized over

Description

Usage

Value

Arguments

Author

Details

References

Examples