vdraw_sc_step_regular

Simulate a piecewise constant-rate Poisson Point Process over <code>(t_min, t_max]</code> (inversion method)
where the intervals have the same length (are "regular").

Simulates events from one dimensional nonhomogeneous Poisson point processes (NHPPPs). Functions are based on three algorithms that provably sample from a target NHPPP: the time-transformation of a homogeneous Poisson process (of intensity one) via the inverse of the integrated intensity function (Cinlar E, "Theory of stochastic processes" (1975, ISBN:0486497996)); the generation of a Poisson number of order statistics from a fixed density function; and the thinning of a majorizing NHPPP via an acceptance-rejection scheme (Lewis PAW, Shedler, GS (1979) <doi:10.1002/nav.3800260304>).

Thomas Trikalinos

nhppp

Simulating Nonhomogeneous Poisson Point Processes

Yuliia Sereda

vdraw_sc_step_regular function

<dl><dt>Lambda_matrix</dt>
<dd>(matrix) integrated intensity rates at the end of each interval</dd>
<dt>lambda_matrix</dt>
<dd>(matrix) intensity rates, one per interval</dd>
<dt>range_t</dt>
<dd>(vector, double) <code>t_min</code> and <code>t_max</code></dd>
<dt>tol</dt>
<dd>(scalar, double) tolerance for the number of events</dd>
<dt>atmost1</dt>
<dd>boolean, draw at most 1 event time</dd></dl>

Arguments

Vectorized sampling from NHPPPs with piecewise constant intensities
with same interval lengths — vdraw_sc_step_regular

<dl>

<dt>Lambda_matrix</dt>
<dd>(matrix) integrated intensity rates at the end of each interval</dd>


<dt>lambda_matrix</dt>
<dd>(matrix) intensity rates, one per interval</dd>


<dt>range_t</dt>
<dd>(vector, double) <code>t_min</code> and <code>t_max</code></dd>


<dt>tol</dt>
<dd>(scalar, double) tolerance for the number of events</dd>


<dt>atmost1</dt>
<dd>boolean, draw at most 1 event time</dd>

</dl>

vdraw_sc_step_regular: Vectorized sampling from NHPPPs with piecewise constant intensities with same interval lengths

Description

Usage

Value

Arguments

Examples