get_step_majorizer

Return a piecewise constant (step) majorizer for K-Lipschitz functions
over an interval.

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

get_step_majorizer function

<dl><dt>fun</dt>
<dd>A function object with a single argument <code>x</code></dd>
<dt>breaks</dt>
<dd>(vector) The set of <code>M+1</code> boundaries for the <code>M</code> subintervals in <code>x</code></dd>
<dt>is_monotone</dt>
<dd>(boolean) Is the function monotone? (Default is <code>TRUE</code>.)</dd>
<dt>K</dt>
<dd>(double) A non-negative number for the Lipschitz cone. (Default is 0.)</dd></dl>

Arguments

Piecewise constant (step) majorizer for K-Lipschitz functions over an interval — get_step_majorizer

<dl>

<dt>fun</dt>
<dd>A function object with a single argument <code>x</code></dd>


<dt>breaks</dt>
<dd>(vector) The set of <code>M+1</code> boundaries for the <code>M</code> subintervals in <code>x</code></dd>


<dt>is_monotone</dt>
<dd>(boolean) Is the function monotone? (Default is <code>TRUE</code>.)</dd>


<dt>K</dt>
<dd>(double) A non-negative number for the Lipschitz cone. (Default is 0.)</dd>

</dl>

get_step_majorizer: Piecewise constant (step) majorizer for K-Lipschitz functions over an interval

Description

Usage

Value

Arguments

Examples