Classical Probability with a falling ball problem

In summary, the conversation is about a worksheet on the similarity between classical probability and the probability of finding a particle in a box. The picture shows a ball falling down two platforms with different constant velocities. The probability of the ball being found on each platform is shown in the picture. The next question involves finding the probability of the ball being between x1 and x1+delta x, where x1 is between 0 and L1, and delta x is small. The approach involves using the probability of two independent events occurring and plotting the probability vs x. The question also asks about finding the probability between x1 and x1+(delta x)/2. There is a discussion about the relationship between velocity and probability, with one person suggesting that
  • #1
dweeegs
12
1
http://i793.photobucket.com/albums/yy215/dweeegs/probability_zps12a67dfb.png

The picture shows everything needed.

This is a worksheet on the similarity of classical probability to the probability of finding a particle in a box (Schrodinger stuff etc)

Basically there's a ball falling down; it has a constant velocity on one platform and gains velocity going to another platform (where it's also constant). So two platforms and a ball has a different velocity on each (specifically the velocity on the second platform was found to be twice that on the first).

I found the probability that the ball will be found on each platform (pictured). The next question is giving me some problems since I haven't really taken stat in a while:

Use the answer I found in the picture to find P(x1,delta x), the probability of finding a ball between x1 and x1+delta x, where x1 is between 0 and L1 (the first platform), and delta x is small.

I'm clueless on how to approach this :/

The probability of two independent events occurring is the multiplication of those two probabilities, but I don't know how that would involve x1 or delta x, and it says to plot the probability vs x. But on second thought it might make sense because since the velocities are constant, the probability is constant.

It then goes on to ask about finding the probability between x1 and x1+(delta x)/2 and plot that as well, but I'm not sure how that would differ from the first question.

Am I on the right track? Any help?
 
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  • #2
I don't understand what the relationship is assumed to be between the velocity at a location and the probability of being there at an instant. you seem to be taking them as directly proportional - is that right? (Why?) Inversely proportional would be more intuitive.
 

Related to Classical Probability with a falling ball problem

1. What is classical probability?

Classical probability, also known as a priori probability, is a branch of probability theory that deals with events that have equally likely outcomes. It is based on the assumption that all possible outcomes are known and each has an equal chance of occurring.

2. What is the falling ball problem in classical probability?

The falling ball problem is a classic example used to illustrate the concept of classical probability. It involves dropping a ball from a certain height and determining the probability of it landing in a specific area or zone.

3. How is the probability of a falling ball problem calculated?

The probability of a falling ball problem is calculated by dividing the number of desired outcomes by the total number of possible outcomes. For example, if there are 5 possible outcomes and 2 of them result in the ball landing in the desired zone, the probability would be 2/5 or 40%.

4. What are some real-world applications of classical probability with the falling ball problem?

Classical probability with the falling ball problem has various real-world applications, such as predicting the likelihood of a sports team winning a game, determining the probability of a stock market trend, or estimating the chances of a medical treatment being successful.

5. How does classical probability differ from other types of probability?

Classical probability differs from other types of probability, such as empirical or subjective probability, in that it is based on known, equally likely outcomes. It also does not take into account any additional information or data, unlike other types of probability which may use past events or personal beliefs to determine likelihood.

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