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Random
Process-
Introduction
Ms.S.S.Halunde
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.
2
3
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.
4
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.
5
6
7
What is probability?
8
9
10
11
12
13
14
15
Total Probability
16
Bayes’ Formula
17
18
19
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.
20
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.
21
22
23
Thanks!
Any questions?
24

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DCOM_3_Random process1.pptx

  • 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. 2
  • 3. 3 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.
  • 4. 4 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.
  • 5. 5
  • 6. 6
  • 8. 8
  • 9. 9
  • 10. 10
  • 11. 11
  • 12. 12
  • 13. 13
  • 14. 14
  • 17. 17
  • 18. 18
  • 19. 19 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.
  • 20. 20 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.
  • 21. 21
  • 22. 22
  • 23. 23