A simple probability problem involving the law of total probability

In summary: R_{n}) = pR_{n}.This is the same as question 1) because the relevant probability only depends on the previous day and not on the whole history. Thus, you can use your result from question 1) to answer question 3).In summary, the problem introduces a simple meteorological model where the probability of rain on a given day is dependent on whether it rained the previous day. The probability of rain on a given day is denoted by R and the complement of R is denoted by Rc. If the probability of rain on a given day is p, the probability of rain on the next day is p(α-1+B) + (1-B) where α and β
  • #1
calorimetry
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Homework Statement



This problem introduces a simple meteorological model, more complicated versions of which have been proposed in the meteorological literature. Consider a sequence of days and let Ri denote the event that it rains on day i . Suppose that P(Ri | Ri−1) = α and P(Rci | Rci−1c) = β.

Note that Rci is the complement of Ri.

Suppose further that only today’s weather is relevant to predicting tomorrow’s; that is, P(Ri| Ri−1 ∩ Ri−2 ∩···∩ R0) = P(Ri| Ri−1).

a. If the probability of rain today is p, what is the probability of rain tomorrow?
b. What is the probability of rain the day after tomorrow?
c. What is the probability of rain n days fromnow?What happens as n approaches infinity?

Homework Equations



The Attempt at a Solution



Part (a) is pretty easy, I just apply the total probability formula and it should give me the answer.
P(R1) = P(R1 | R0)*P(R0) + P(R1 | Rc0)*P(Rc0)
P(R1) = α*p + (1-B)*(1-p) = p(α-1+B) + (1-B)

Part (b) is tedious if I just apply the total probability formula again and use P(R1) I found in part (a).
P(R2) = p(α-1+B)^2 + (1-B)(α+B)

Part (c), since there appears to be a repetitive pattern in the answer in (b) and (a), I solved for P(R3) to try and confirm this, and got
P(R3) = p(α-1+B)^3 + (1-B)[(α+B)(α-1) + 1]

so I'm thinking that my method in approaching the last part in inaccurate because I can't see the pattern and I haven't used the other piece of information that was given.

Suppose further that only today’s weather is relevant to predicting tomorrow’s; that is, P(Ri| Ri−1 ∩ Ri−2 ∩···∩ R0) = P(Ri| Ri−1).

Any hint in the right direction is appreciated.
Thanks.
 
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  • #2
Hi calorimetry.

Question 3) is slightly different from questions 1) and 2). There, they asked you the probability of raining on one fixed day ([tex]pR_{1}[/tex] for question 1) and [tex]pR_{2}[/tex] for question 2)).

Here, they are asking you for the probability of raining on n days from now, so is

[tex]p(\bigcap^{n}_{i=1}R_{i})[/tex]
 

1. What is the Law of Total Probability?

The Law of Total Probability is a mathematical concept that states that the probability of an event occurring is equal to the sum of the probabilities of all possible outcomes that could lead to that event.

2. How is the Law of Total Probability used in probability problems?

The Law of Total Probability is often used to solve complex probability problems by breaking them down into smaller, more manageable parts. By considering all possible outcomes, it allows for a more comprehensive analysis of the problem.

3. Can you provide an example of a simple probability problem involving the Law of Total Probability?

Sure, let's say we have a bag with 3 red marbles and 5 blue marbles. What is the probability of randomly selecting a red marble and then a blue marble? To solve this, we would use the Law of Total Probability and calculate the probability of selecting a red marble (3/8) and then a blue marble (5/7) and multiply them together (3/8 x 5/7 = 15/56).

4. Are there any limitations to the Law of Total Probability?

One limitation of the Law of Total Probability is that it assumes all outcomes are equally likely. This may not always be the case in real-world scenarios, so it is important to consider the context of the problem when applying this law.

5. How does the Law of Total Probability relate to other probability laws?

The Law of Total Probability is closely related to other probability laws, such as the Law of Complements and the Law of Independent Events. It can also be used in conjunction with other laws, such as Bayes' Theorem, to solve more complex probability problems.

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