Suppose that you have a stochastic process that takes discrete values eg outcomes of. A discrete random variable can take on an exact value while the value of a continuous random variable will fall between some particular interval.
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The answer depends on whether you are dealing with discrete or continuous random variables.
. As a consequence a probability mass function is used to describe a discrete random variable and a probability density function describes a continuous random variable. Introduction to z-plane stability criteria. Let a coin be tossed ten times.
To help see the difference between continuous and discrete variables imagine a really tall mountain with a trail leading up to the top. Examples of distributions with discrete random variable are. If a random variable takes all possible values between certain given limits it is called as continuous random variable.
For example most analysts would treat the number of heart beats per minute as continuous even though it is a count. Discrete Random Variable. Continuous Random Variable.
Because the view is so wonderful lots of people want to go. The main benefit of treating a discrete variable with many different unique values as continuous is to assume the Gaussian. The transfer function and the difference equation.
The Inverse z-Transform 15 Frequency response and poles and zeros. FIR low-pass filter design 16. Note even though a variable may discrete if the variable takes on enough different values it is often treated as continuous.
The frequency response of discrete-time systems. If X be the. I will assume that you want some technical details and not necessarily an explanation in plain English.
So I will split my answer accordingly. The discrete-time convolution sum. The z-transform 14 The discrete-time transfer function.
If a random variable takes only a finite or countable number of values it is called as discrete random variable.
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