Calculating Probabilities for Independent Bernoulli Trials

In summary, the conversation discusses the derivation of the probability mass function of a random variable X, which represents the number of failures before the first success in a series of independent Bernoulli trials with success probability p. It is noted that the probability mass function is a geometric sequence, given by p(x) = p(1-p)^x for x = 0,1,2... The second question asks for the probability that X is less than a positive integer x, which is equivalent to the cumulative distribution function of the geometric sequence. There is some confusion about whether this can be represented by X~binomial(n,p) or as the sum of probabilities from 0 to x-1.
  • #1
playboy
A Question Reads: "Suppose that the random Variable X is the number of failures before the first success in a series of independent Bernoulli trials with success probability p"

a) derive the probability mass function of X
b) what is the probability that X < x where x is a positive integer?

My Answers:

a) this is fairly straight forward. Its just a geometric sequence. p(x) = p(1-p)^x x = 0,1,2...

b) I AM STUCK ON THIS. I know that X < x is just the cumulative distribution function for this geometric sequence, but that just does not work well with this. I tried something different:

"the number of failures until the first sucess? P(X<x) for a geometric sequnce? that means x-1 failures in n trials? (or we could think of it as x-1 sucess in n trials if we look at a failure as a success.)
then would it be X~binomial(n,p)"

Or perhaps it means P(X=0) + P (X=1) + P(X=2) + P(X=3)... + P(X=x-1) for a geometric sequence?

Can somebody help me on it please? thanks!
 
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  • #2
I just found out now that my a) is wrong :( my prof said its not geometric, but is close to it... anybody have any ideas?
 

FAQ: Calculating Probabilities for Independent Bernoulli Trials

1. What is probability theory?

Probability theory is a branch of mathematics that deals with the study of random events and how likely they are to occur. It is used to model and analyze situations where there is uncertainty or unpredictability.

2. What are the basic concepts of probability theory?

The basic concepts of probability theory include sample space, events, and probabilities. The sample space is the set of all possible outcomes of an experiment. Events are subsets of the sample space and probabilities assign a numerical value to each event, representing the likelihood of that event occurring.

3. What are the different types of probability?

There are three types of probability: classical, empirical, and subjective. Classical probability is based on equally likely outcomes, empirical probability is based on observations and data, and subjective probability is based on personal judgments or beliefs.

4. How is probability calculated?

Probability is calculated by dividing the number of favorable outcomes by the total number of possible outcomes. It can be represented as a fraction, decimal, or percentage.

5. What are some real-life applications of probability theory?

Probability theory has many real-life applications, such as in gambling, insurance, weather forecasting, and stock market analysis. It is also used in fields like engineering, physics, and biology to model and analyze uncertain systems and events.

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