# Symbols:Abbreviations/P

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## P

### PCI

Principle of Complete Induction (or Principle of Complete Finite Induction).

### PCFI

Principle of Complete Finite Induction.

### PDE

A partial differential equation (abbreviated P.D.E. or PDE) is a differential equation which has:

one dependent variable
more than one independent variable.

The derivatives occurring in it are therefore partial.

### pdf

Probability density function:

Let $\struct {\Omega, \Sigma, \Pr}$ be a probability space.

Let $X: \Omega \to \R$ be a continuous random variable on $\struct {\Omega, \Sigma, \Pr}$.

Let $\Omega_X = \Img X$, the image of $X$.

Then the probability density function of $X$ is the mapping $f_X: \R \to \closedint 0 1$ defined as:

$\forall x \in \R: \map {f_X} x = \begin {cases} \displaystyle \lim_{\epsilon \mathop \to 0^+} \frac {\map \Pr {x - \frac \epsilon 2 \le X \le x + \frac \epsilon 2} } \epsilon & : x \in \Omega_X \\ 0 & : x \notin \Omega_X \end {cases}$

### PFI

Principle of Finite Induction.

### PGF or p.g.f.

Probability generating function:

Let $X$ be a discrete random variable whose codomain, $\Omega_X$, is a subset of the natural numbers $\N$.

Let $p_X$ be the probability mass function for $X$.

The probability generating function for $X$, denoted $\map {\Pi_X} s$, is the formal power series defined by:

$\displaystyle \map {\Pi_X} s := \sum_{n \mathop = 0}^\infty \map {p_X} n s^n \in \R \left[\left[{s}\right]\right]$

### PID or pid

Principal ideal domain:

A principal ideal domain is an integral domain in which every ideal is a principal ideal.

### PMF or p.m.f.

Probability mass function:

Let $\struct {\Omega, \Sigma, \Pr}$ be a probability space.

Let $X: \Omega \to \R$ be a discrete random variable on $\struct {\Omega, \Sigma, \Pr}$.

Then the (probability) mass function of $X$ is the (real-valued) function $p_X: \R \to \closedint 0 1$ defined as:

$\forall x \in \R: \map {p_X} x = \begin{cases} \map \Pr {\set {\omega \in \Omega: \map X \omega = x} } & : x \in \Omega_X \\ 0 & : x \notin \Omega_X \end{cases}$

where $\Omega_X$ is defined as $\Img X$, the image of $X$.

That is, $\map {p_X} x$ is the probability that the discrete random variable $X$ takes the value $x$.

### PMI

Principle of Mathematical Induction.

### PNT

Prime Number Theorem.

### Poset

Partially Ordered Set.

A partially ordered set is a relational structure $\left({S, \preceq}\right)$ such that $\preceq$ is a partial ordering.

The partially ordered set $\left({S, \preceq}\right)$ is said to be partially ordered by $\preceq$.

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