compute_random_walk

A method for the Bayesian functional linear regression model (scalar-on-function),
including two estimators of the coefficient function and an estimator of its support.
A representation of the posterior distribution is also available. Grollemund P-M., Abraham C.,
Baragatti M., Pudlo P. (2019) <doi:10.1214/18-BA1095>.

Paul-Marie Grollemund

bliss

Bayesian Functional Linear Regression with Sparse Step Functions

Isabelle Sanchez

Meili Baragatti

compute_random_walk function

<dl><dt>n</dt>
<dd>an integer, the number of random walks.</dd>
<dt>p</dt>
<dd>an integer, the length of the random walks.</dd>
<dt>mu</dt>
<dd>a numerical vector, the mean of the random walks.</dd>
<dt>sigma</dt>
<dd>a numerical value which is the standard deviation of the
gaussian distribution used to compute the random walks.</dd>
<dt>start</dt>
<dd>a numerical vector (optional) which is the initial value of
the random walks.</dd></dl>

Arguments

compute_random_walk — compute_random_walk

<dl>

<dt>n</dt>
<dd>an integer, the number of random walks.</dd>


<dt>p</dt>
<dd>an integer, the length of the random walks.</dd>


<dt>mu</dt>
<dd>a numerical vector, the mean of the random walks.</dd>


<dt>sigma</dt>
<dd>a numerical value which is the standard deviation of the
gaussian distribution used to compute the random walks.</dd>


<dt>start</dt>
<dd>a numerical vector (optional) which is the initial value of
the random walks.</dd>

</dl>

compute_random_walk: compute_random_walk

Description

Usage

Value

Arguments

Details

Examples