get_grad_trans

Construct parameter transformation for parameters to be optimized over in <code>AIUQ</code>
method of <code>SAM</code> class. See 'References'.

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_grad_trans function

<dl><dt>theta</dt>
<dd>parameters to be optimized over</dd>
<dt>d_input</dt>
<dd>sequence of lag times</dd>
<dt>model_name</dt>
<dd>model name for the fitted model, options from ('BM','OU',
'FBM',OU+FBM','user_defined')</dd></dl>

Arguments

Author

Construct parameter transformation for optimization using exact gradient — get_grad_trans

<dl>

<dt>theta</dt>
<dd>parameters to be optimized over</dd>


<dt>d_input</dt>
<dd>sequence of lag times</dd>


<dt>model_name</dt>
<dd>model name for the fitted model, options from ('BM','OU',
'FBM',OU+FBM','user_defined')</dd>

</dl>

get_grad_trans: Construct parameter transformation for optimization using exact gradient

Description

Usage

Value

Arguments

Author

References