# 20.Basics of Renewal Theory

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# What does the renewal process describe?

Replacement of component.

## failure time

A population of components, the failure time of each component is characterized by a non-negative random variable X. The failure time is in fact the age of the component, defining when the failure occurs.

The distribution of X could be either discrete or continuous. Discrete: X~{0,h,2h,3h,…} Continuous: the probability is determined by a probability density function (pdf) over the range of (0,$\infty$).

## probability density function of X

with

And the failure times are independent.

## Other functions

cumulative distribution function $F(x)$:

and $f(x)=F’(x)$

survivor function $\mathscr{F}(x)$: $\mathscr{F}(x)= \mathrm{prob(X>x)}\\ = 1-F(x)\\ = \int_x^{\infty} f(u) du$

$f(x)=-\mathscr{F}’(x)$

hazard function: $\phi (x)$ the probability of almost immediate lailure of a component at age $x$.

so, $% $

# Discrete time: Life table events

A life table consists of a list of the number of individuals, usually from an initial group of 1000 individuals so that the numbers are effectively proportions, who survive to a given age in a given population.

Important parameters:

$\mathscr{l}_x$: surviving to age $x$

$d_x$: dying between age x and x+1

$d_x = \mathscr{x}-\mathscr{x+1}$

$q_x$: those surviving to age $x$ who die before reaching age $x+1$ $q_x = d_x/\mathscr{l}_x$

# neural spike train

In the presence of noise, the spike train generation is a stochastic point process, not deterministic. Hence the probability of generating the next event (spike), depends only on the “age” $t−\hat t$ of the system, i.e., the time that has passed since the last event (last spike).

The central assumption of renewal theory is that the state does not depend on earlier events .