Applying Shannons Expansion Theorem

In summary, the conversation is about applying Shannon's expansion theorem to a given function using a 2:1 MUX and finding the minimum equations for two different inputs. The final solution is ~y(~w~x + ~w~z) + y(w + w~z).
  • #1
shamieh
539
0
Wasn't exactly sure where to post this. Wanted to see if I did this correctly.Can someone check my work please?

Problem: Consider f defined below. Apply Shannon's expansion theorem (also given below) with respect to input y as if you were implementing this function using a 2:1 MUX. Find the minimum equations for f(w,x,0,z) and f(w,x,1,z).
Shannons Expansion Theorem
f(w_1, w_2,...,w_n) = ~w_1 * f(0,w_2,...,w_n) + w_1 * f(1,w_2,...,w_n)

The function to be expanded: f(w,x,y,z) = wy + ~w~x~y + ~w~y~z + wy~z
Here is what I got for my solution.

~y(~w~x + ~w~z + ~wz) + y(w + w~z)
 
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  • #2
Why do you have the term ~wz in the first part, the one that corresponds to y = 0?
 
  • #3
After re-doing the problem I obtained this: ~y(~w~x + ~w~z) + y(w + w~z)
 
Last edited:
  • #4
That's correct.
 
  • #5


For f(w,x,0,z):
~0(~w~x + ~w~z + ~wz) + 0(w + w~z)
= (~w~x + ~w~z + ~wz)

For f(w,x,1,z):
~1(~w~x + ~w~z + ~wz) + 1(w + w~z)
= (w + w~z)
 

Related to Applying Shannons Expansion Theorem

1. What is Shannons Expansion Theorem?

Shannons Expansion Theorem, also known as the Maximum Entropy Theorem, is a mathematical principle that allows us to estimate the probability of an event based on known information.

2. How is Shannons Expansion Theorem applied?

Shannons Expansion Theorem is applied by using the known information to create a probability distribution and then finding the distribution with the maximum entropy. This distribution is then used to estimate the probability of the event in question.

3. What are the assumptions behind Shannons Expansion Theorem?

There are three main assumptions behind Shannons Expansion Theorem: 1) all possible outcomes of the event are equally likely, 2) the event is independent of any other events, and 3) the known information is accurate and complete.

4. What are the limitations of Shannons Expansion Theorem?

One major limitation of Shannons Expansion Theorem is that it requires a large amount of known information in order to accurately estimate the probability of an event. Additionally, it assumes that all outcomes are equally likely, which may not always be the case in real-world situations.

5. What are some practical applications of Shannons Expansion Theorem?

Shannons Expansion Theorem has many practical applications in fields such as statistics, information theory, and machine learning. It is commonly used to analyze and predict the outcomes of complex systems, such as in weather forecasting and financial markets.

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