Probability - Exponential Function Question

In summary, the sensor can detect mollusks within a 4 meter radius. The p.d.f. of the distribution of detected mollusks is approximately 0.7.
  • #1
tokuroka
6
0

Homework Statement


A particular species of mollusk is distributed according to a Poisson process with an unknown density of lambda per cubic meter of water. A sensor is constructed that can detect these species that are within 4 m, and n readings are collected over a year to try and measure lambda. These readings are independent, and so the number of readings that detect a mollusk has the distribution X~Bi(n,p) for some unknown value of p.

p is estimated to be approximately 0.7.

Problem: Let Y be the distance from the sensor to the nearest mollusk.
Show that the c.d.f. of Y is of the form
F(y) = 1- e-(lambda)(4/3pi y3 if y >0.

b) Find the p.d.f. of Y and use it to compute E(Y).

Homework Equations


gamma(4/3) = 0.8929795

The Attempt at a Solution


I'm not so much worried about part a), but I tried part b) and I'm getting nowhere. I took the derivative of F(y) to get f(y) (the p.d.f) and got f(y) = 4pi(lambda) y^2(e-(lambda)(4/3 pi*y3, and then for E(Y), I multiplied that by y and took the integral from 0 to infinity of that. I read in my textbook that for the basic proof of expected value, you're supposed to make a substitution for theta = 1/lambda, and then substitute more values to make this integral look like the gamma function. So I substituted x = 4pi/3theta y^3 (to make the power of e = -x) and then what I am left with after simplifying is... the integral from 0 to infinity of e^-x dx... I'm pretty sure that's wrong though... since the problem tells me I should be using the gamma function of 4/3 for something.. Can anyone help me with this? Thanks.
 
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  • #2
Did you write dy in terms of dx?
 
  • #3
yeah I did.. then everything canceled out.
 
  • #4
Sounds like you didn't multiply by y then. You calculated E(1), not E(y).
 
  • #5
Ah, okay, I solved it now. Thanks.
 

Related to Probability - Exponential Function Question

1. What is the exponential function in probability?

The exponential function in probability is a mathematical formula used to model the probability of an event occurring over a continuous period of time. It is often used to describe the distribution of waiting times between events, such as the time between arrivals at a store or the time between failures of a machine.

2. How is the exponential function calculated?

The exponential function is calculated by raising the base number (usually denoted as e) to the power of the exponent, which represents the continuous time period. The formula is expressed as P(t) = e-λt, where P(t) is the probability of the event occurring at time t and λ is the rate parameter.

3. What is the relationship between the exponential function and the Poisson distribution?

The Poisson distribution is often used in conjunction with the exponential function to model the number of events occurring within a given time period. The exponential function describes the time between events, while the Poisson distribution determines the number of events occurring within that time period. Together, they can be used to model a wide range of real-world phenomena, such as the number of customers arriving at a store in a given hour.

4. How is the exponential function used in real life?

The exponential function is used in many real-life applications, such as predicting the arrival times of buses or estimating the time between failures of a machine. It is also commonly used in finance to model the growth or decay of investments over time. In epidemiology, the exponential function is used to model the spread of infectious diseases.

5. What are the limitations of the exponential function in probability?

The exponential function assumes that the probability of an event occurring is constant over time, which may not always be the case in real life. It also assumes that events occur independently of each other, which may not always be true. Additionally, the exponential function can only be used for continuous time periods and may not accurately model discrete events. It is important to carefully consider the assumptions and limitations of the exponential function when using it in probability calculations.

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