# Continuous-Time Discrete-State Stochastic Decay

# Continuous-Time Discrete-State Stochastic Decay

## Continuous-Time Markov Chain of Degradation, Decay, or Departure

### Introduction

A simple **homogeneous **degradation, decay (e.g. memory decay), death, or departure **process **can be described as a directed graphical model (stoichiometric equation in chemics):

$$ N_A \stackrel{\lambda}{\longrightarrow} {\varnothing} \; \; \; \qquad (1)$$

where:

$$ A \text{ := is a species of interest} $$

$$ N_A \text{ := is the number of particles of species of interest} $$

$$ \lambda \text{ := is the rate constant of degradation events} $$

$$ \varnothing \text{ := is a species of no special interest} $$

In a ** homogeneous process** the rate is defined as a

*constant*$$\lambda$$ so that $$\lambda \cdot dt $$ gives the

*probability*that a randomly chosen particle of A reacts, degrades, dies, decays, or departs during the time interval $$[t, t + dt) $$ where

*t*are time and

*dt*an (infinitesimally) small time step.

In an ** in- or nonhomogeneous process** the rate is dependent on

*time*or

*state*$$\lambda(t) \text{ or } \lambda(N_A(t)) $$ so that $$\lambda(t) \cdot dt \text{ or } \lambda(N_A(t)) \cdot dt $$ gives the

*probability*that a randomly chosen particle of A reacts, degrades, dies, decays, or departs during the time interval $$[t, t + dt) $$ where

*t*are time and

*dt*an (infinitesimally) small time step.

The stoichiometric equations or the DAGs are now:

$$ N_A \stackrel{\lambda(t)}{\longrightarrow} {\varnothing} \; \; \; \;\text{ with time-dependent rate } \qquad (2.1)$$

$$ N_A \stackrel{\lambda(N_A)}{\longrightarrow} {\varnothing} \; \; \; \text{ with state-dependent rate } \qquad (2.2)$$

### The Definition of the (In-)Homogeneous Counter Decay Process

The "naive" implementation of the solution of the decay counter process is based not on *dt* but on a discrete approximation $$\Delta t >> dt$$ so it is wise to reuse and modify the second definition of the Poisson process. The decay process $$N_A(t), t \in [0, \infty)$$ is called a **homogeneous counter decay process** with rate $$\lambda > 0 $$ if all of the following conditions hold:

$$1. \;\;N_A(0) = N_{A_0} > 0\; \; \text{ ; } $$

$$2. \;\;N_A(t) \text{ has independent and stationary increments} $$

then we have

$$P(N_A(\Delta t) = 0) \approx 1 - \lambda \Delta t \qquad (3.1.1)$$

$$P(N_A(\Delta t) = -1) \approx \lambda \Delta t \qquad (3.1.2)$$

$$P(N_A(\Delta t) = -2) \approx 0 \qquad (3.1.3)$$

or more precisely

$$P(N_A(\Delta t) = 0) = 1 - \lambda \Delta t + o(\Delta t) \qquad (3.2.1)$$

$$P(N_A(\Delta t) = -1) = \lambda \Delta t + o(\Delta t) \qquad (3.2.2)$$

$$P(N_A(\Delta t) = -2) = o(\Delta t) \qquad (3.2.3)$$

where "little o" denotes a function g(.) $$ o(\Delta t) := g(\Delta t) $$ that 'vanishes' faster than $$\Delta t $$ when

$$ \lim_{\Delta t \to 0} \frac{g(\Delta t)}{\Delta t} = 0, \qquad (4) $$

or short

$$ o(\Delta t) \rightarrow 0 \text{ as } \Delta t \rightarrow 0 \; \; .$$

## WebPPL-Script: Stochastic Simulation of (In-)Homogeneous Process with Constant Time Increment Delta

## Simulation Runs as an Inhomogeneous Pure Decay Process

### Fig. 1: Parameters of Inhomogeneous Process with State-dependent Rates

### Fig. 2 - 11: Sequence of 10 Independent Simulation Runs of an Inhomogeneous Pure Decay Process

## Simulation Runs as an Homogeneous Pure Decay Process

### Fig. 1: Parameters of Homogenous Process

### Fig. 2-11 Independent Runs