Probability Question (Random Variables and CDF)

In summary, the transmission channel is noisy and has a probability of .11 of incorrectly transmitting a binary bit. A majority decoder is used to determine which bit was received the majority of the time after being sent n (odd) times. The distribution of errors, X, can be represented by p_{x}(x)=.11^{x}.89^{n-x}. The probability of correctly receiving the message for n=25 is calculated by finding the sum of the probability
  • #1
swuster
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Homework Statement


A transmission channel is noisy and a binary bit (assume it is a 0 or a 1) has probability of .11 of being incorrectly transmitted. Suppose the bit is sent n (odd) times and a majority decoder announces which bit is received the majority of the time. Assume retransmissions constitute Bernoulli trials.
(a) Let X be the number of errors in n transmissions. Give a formula for the distribution of X.
(b) What is the probability the message is correctly received, for n=25?

Homework Equations


n/a

The Attempt at a Solution


X is discrete, so for part (a) I came up with [tex]p_{x}(x)=.11^{x}.89^{n-x}[/tex] which I'm not convinced is totally right.

For part b I want to calculate [tex]P(X\leq12)[/tex] since this is the probability that the message is correctly received (number of errors is less than half). But if I attempt to calculate the cumulative distribution function using my distribution, I get [tex]\sum^{12}_{x=1}.11^{x}.89^{n-x} = .06195[/tex] which is clearly way too low. Any help?
 
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  • #2
hmm, I really confused with the words (my english fault). anyway, what i know about bernoulli's trial is that there's only chance of sucess and failure..

So, the equation of bernoulli's trial f(x)=(p)x(1-p)1-x , x=0,1

so, in this case,
f(x)=(nCx)(0.11)x(0.89)n-x , x=1,2,...,n

because of n times,
 

FAQ: Probability Question (Random Variables and CDF)

1. What is a random variable?

A random variable is a numerical quantity that takes on different values based on the outcomes of a random event. It can be discrete, meaning it can only take on a finite or countably infinite set of values, or continuous, meaning it can take on any value within a certain range. In probability, random variables are used to represent uncertain outcomes and are essential in calculating probabilities and expected values.

2. What is a probability distribution?

A probability distribution is a mathematical function that describes the likelihood of different outcomes of a random variable. It assigns probabilities to each possible outcome, with the sum of all probabilities equal to 1. There are two types of probability distributions: discrete and continuous. Discrete probability distributions are used for random variables that can only take on a finite or countably infinite set of values, while continuous probability distributions are used for random variables that can take on any value within a certain range.

3. What is a cumulative distribution function (CDF)?

A cumulative distribution function (CDF) is a function that shows the probability that a random variable takes on a value less than or equal to a given value. It is also known as the probability distribution function for continuous random variables. The CDF is used to determine the probability of a random variable falling within a certain range of values, and it can also be used to calculate the probability of specific outcomes.

4. How is a CDF different from a probability distribution?

A CDF and a probability distribution are related but different concepts. A probability distribution assigns probabilities to each possible outcome of a random variable, while a CDF shows the cumulative probability of a random variable taking on a value less than or equal to a given value. The probability distribution is a function, while the CDF is a cumulative function that is used to determine the probability of a random variable falling within a certain range of values.

5. How can probability be used in real-life situations?

Probability is used in many real-life situations, such as predicting the outcome of a sports game, determining the likelihood of an event occurring (such as winning the lottery), and making decisions based on uncertain outcomes. It is also used in fields such as finance, insurance, and weather forecasting. Understanding probability can help us make informed decisions and assess risks in various situations.

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