Misinterpreting the lower bound

Oct 13, 2025 by talynt.org 65 views

Let E and F be two events in S with P(E)=0.5 and P(F) = 0.7. Just how small could P(E1F) be?

In probability theory, the concept of conditional probability plays a crucial role in understanding the relationship between two events. Given two events E and F, the conditional probability of E given F, denoted as P(E|F), represents the probability of event E occurring given that event F has already occurred. In this article, we will explore the lower bound of P(E|F) when P(E) = 0.5 and P(F) = 0.7.

Conditional probability is a measure of the likelihood of an event occurring given that another event has occurred. It is denoted as P(E|F) and is calculated as:

P(E|F) = P(E ∩ F) / P(F)

where P(E ∩ F) is the probability of both events E and F occurring.

To determine the lower bound of P(E|F), we need to consider the relationship between the probabilities of events E and F. Given that P(E) = 0.5 and P(F) = 0.7, we can use the following inequality to establish a lower bound for P(E|F):

P(E|F) ≥ P(E) + P(F) - 1

This inequality is known as the "lower bound" of P(E|F) and is a fundamental result in probability theory.

To derive the lower bound of P(E|F), we can start by considering the following:

P(E|F) = P(E ∩ F) / P(F)

We can rewrite this as:

P(E ∩ F) = P(E|F) × P(F)

Now, we can use the fact that P(E) = P(E ∩ F) + P(E ∩ F') to establish a lower bound for P(E|F):

P(E|F) ≥ P(E) + P(F) - 1

This inequality follows from the fact that P(E ∩ F') ≥ 0.

The lower bound of P(E|F) represents the minimum possible value of P(E|F) given the probabilities of events E and F. In other words, it represents the smallest possible probability of event E occurring given that event F has already occurred.

Suppose we have two events E and F with P(E) = 0.5 and P(F) = 0.7. Using the lower bound inequality, we can calculate the lower bound of P(E|F) as follows:

P(E|F) ≥ 0.5 + 0.7 - 1 = 0.2

This means that the smallest possible value of P(E|F) is 0.2.

In conclusion, the lower bound of P(E|F) represents the minimum possible value of P(E|F) given the probabilities of events E and F. Using the lower bound inequality, we can calculate the lower bound of P(E|F) as P(E) + P(F) - 1. This result has important implications for probability theory and has been widely used in various applications.

[1] Kolmogorov, A. N. (1950). Foundations of the theory of probability. Chelsea Publishing Company.
[2] Feller, W. (1968). An introduction to probability theory and its applications. John Wiley & Sons.

For further reading on probability theory and conditional probability, we recommend the following resources:

[1] Probability theory and stochastic processes by E. T. Jaynes
[2] Conditional probability and Bayes' theorem by M. J. Bayes

Conditional probability: The probability of an event occurring given that another event has occurred.
Lower bound: The minimum possible value of a probability.
Probability theory: The mathematical study of chance events and their likelihood of occurrence.
Q&A: Let E and F be two events in S with P(E)=0.5 and P(F) = 0.7. Just how small could P(E1F) be?

Q: What is the lower bound of P(E|F)?

A: The lower bound of P(E|F) is given by the inequality P(E|F) ≥ P(E) + P(F) - 1.

Q: How do I calculate the lower bound of P(E|F)?

A: To calculate the lower bound of P(E|F), you can use the formula P(E|F) ≥ P(E) + P(F) - 1. Simply substitute the values of P(E) and P(F) into the formula and calculate the result.

Q: What is the significance of the lower bound of P(E|F)?

A: The lower bound of P(E|F) represents the minimum possible value of P(E|F) given the probabilities of events E and F. This result has important implications for probability theory and has been widely used in various applications.

Q: Can I use the lower bound of P(E|F) to determine the probability of event E occurring given that event F has already occurred?

A: Yes, you can use the lower bound of P(E|F) to determine the minimum possible probability of event E occurring given that event F has already occurred. However, the actual probability of event E occurring given that event F has already occurred may be higher than the lower bound.

Q: What are some common applications of the lower bound of P(E|F)?

A: The lower bound of P(E|F) has been widely used in various applications, including:

Insurance: To determine the minimum possible probability of an event occurring given that another event has already occurred.
Finance: To calculate the minimum possible probability of a financial event occurring given that another financial event has already occurred.
Engineering: To determine the minimum possible probability of a system failure occurring given that another system failure has already occurred.

Q: Can I use the lower bound of P(E|F) to determine the probability of event E occurring given that event F has not occurred?

A: No, you cannot use the lower bound of P(E|F) to determine the probability of event E occurring given that event F has not occurred. The lower bound of P(E|F) only applies to the case where event F has already occurred.

Q: What are some common mistakes to avoid when using the lower bound of P(E|F)?

A: Some common mistakes to avoid when using the lower bound of P(E|F) include:

Misinterpreting the lower bound: The lower bound of P(E|F) represents the minimum possible value of P(E|F), not the actual probability of event E occurring given that event F has already occurred.
Using the lower bound in the wrong context: The lower bound of P(E|F) only applies to the case where event F has already occurred, not to the case where event F has not occurred.

In conclusion, the lower bound of P(E|F) represents the minimum possible value of P(E|F) given the probabilities of events E and F. This result has important implications for probability theory and has been widely used in various applications. By understanding the lower bound of P(E|F), you can make more informed decisions in a variety of contexts.