Random signals can be either deterministic or random, depending on their source and characteristics. Deterministic signals have predictable properties, while random signals have unpredictable properties that require statistical analysis. Examples of random signals include thermal noise from electron movement and periodic signals with random parameters like phase or amplitude. Random variables are values that depend on the outcome of a random experiment. A random variable is a function that assigns a numerical value to each possible outcome in the sample space of an experiment. For example, in a coin toss experiment repeated 20 times, the number of heads obtained is a random variable that can take on values from 0 to 20.

1. Random
Process-
Introduction
Ms.S.S.Halunde

2. RANDOM
SIGNALS
▫ Signals can be divided into two main categories - deterministic
and random. The term random signal is used primarily to
denote signals, which have a random in its nature source. As
an example we can mention the thermal noise, which is
created by the random movement of electrons in an electric
conductor.
▫ The term random signal is used also for signals falling into
other categories, such as periodic signals, which have one or
several parameters that have appropriate random behavior.
▫ An example is a periodic sinusoidal signal with a random
phase or amplitude. Signals can be treated either as
deterministic or random, depending on the application.
Speech, for example, can be considered as a deterministic
signal, if one specific speech waveform is considered. It can
also be viewed as a random process if one considers the
ensemble of all possible speech waveforms in order to design a
system that will optimally process speech signals, in general.
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Random signals can be both analog and digital. The values of
digital signals are represented with a finite number of digits. This
implies that the stochastic terms used are different for the two
signal categories.

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Random variables-
In the study of probability, any process of observation is referred to as
an experiment.
The results of an observation are called outcomes of the experiment.
If the outcomes of an experiment cannot be predicted, then it is called
random experiment.
The set of possible outcomes of a random experiment is called the
sample space.
An element in the sample space is called a sample point. Each outcome
of a random experiment corresponds to a sample point.
Subsets of the sample space are called events, and events consisting of
a single element (sample point) are called elementary events.

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What is probability?

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Total Probability

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Bayes’ Formula

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Random Variables
A variable is something which can change its value. It may vary with
different outcomes of an experiment. If the value of a variable depends
upon the outcome of a random experiment it is a random variable. A
random variable can take up any real value.
Mathematically, a random variable is a real-valued function whose
domain is a sample space S of a random experiment. A random
variable is always denoted by capital letter like X, Y, M etc. The
lowercase letters like x, y, z, m etc. represent the value of the random
variable.

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Consider the random experiment of tossing a coin 20 times.
You will earn Rs. 5 is you get head and will lose Rs. 5 if it a
tail. You and your friend are all set to see who will win the
game by earning more money. Here, we see that the value of
getting head for the coin tossed for 20 times is anything from
zero to twenty. If we denote the number of a head by X, then
X = {0, 1, 2, … , 20}. The probability of getting a head is
always ½.

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24. Thanks!
Any questions?
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