Probability Theory proof help?

In summary, the property P(X = n + k |X > n) = P(X = k) holds true for a geometric(p) random variable, as shown in the given proof. This can be explained by considering X as the first successful trial among independent trials, where the probability of success on n+k trials is equal to the probability of success on k trials due to the independence of the trials and the fact that the first n trials are failures and do not affect the probability of success on the remaining k trials.
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slaux89
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Homework Statement



Let X be a geometric(p) random variable for some p ∈ (0, 1). For n, k ∈ N, show that
P(X = n + k |X > n) = P(X = k).
Now, explain this property using the interpretation of X as the first successful trial
among independent trials each of probability p.



Homework Equations





The Attempt at a Solution



Here's my attempt at the proof. Is this a rigorous enough proof?

Since X is a geometric (p) random variable and since we are given that x>n, we know that the first n trials are failures. Since the first n trials are failed trials and an assumption of the geometric distribution is that X is independent, then we can treat x=0 without changing the probability of P(X = n + k |X > n) = P(X = k). Thus P(X = 0 + k |X > 0) = P(X = k). Since X>0 is always true, we can eliminate this condition and thus. P(X = n + k |X > n) = P(X = k)
 
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  • #2
is true. To explain the property using the interpretation of X as the first successful trial among independent trials, we can say that the probability of a successful trial on n+k trials is equal to the probability of a successful trial on k trials. This is because the first n trials are failures and thus do not affect the probability of a successful trial on the remaining k trials.
 

Related to Probability Theory proof help?

What is probability theory?

Probability theory is a branch of mathematics that deals with the study of random events and the likelihood of their occurrence. It helps us understand and quantify uncertainty in various situations.

How is probability theory used in real life?

Probability theory is used in a variety of fields, including finance, economics, physics, and statistics. It is used to make predictions and informed decisions based on uncertain events and data. For example, it is used in weather forecasting, risk assessment, and gambling.

What is the difference between theoretical and empirical probability?

Theoretical probability is based on mathematical principles and is calculated by dividing the number of favorable outcomes by the total number of possible outcomes. Empirical probability is based on observations or experiments and is calculated by dividing the number of times the event occurs by the total number of trials.

What are the basic concepts in probability theory?

The basic concepts in probability theory include sample space, events, probability, and random variables. Sample space is the set of all possible outcomes, events are subsets of the sample space, probability is the likelihood of an event occurring, and random variables are variables that take on different values based on chance.

What is the difference between dependent and independent events?

Dependent events are events where the occurrence of one event affects the likelihood of the other event happening. Independent events are events where the occurrence of one event does not affect the likelihood of the other event happening. In other words, the outcome of one event does not depend on the outcome of the other event in independent events.

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