Doubt about a probability density

In summary, The needle on a broken car speedometer is free to swing, and bounces perfectly off the pins at either end, so that if you give it a flick it is equally likely to come to rest at any angle between 0 to ##\pi##.
  • #1
Kaguro
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I was trying a problem from Griffith's Introduction to QM. The problem was:

The needle on a broken car speedometer is free to swing, and bounces perfectly off the pins at either end, so that if you give it a flick it is equally likely to come to rest at any angle between 0 to ##\pi##.

a)Find probability density ##\rho##(##\theta##).
b)If x is the projection of needle on the horizontal line and r is length of needle, then find probability density ##\rho##(x)
----------------------------------------------------------
The first one is simply:
##\rho##(##\theta##)##d\theta## = ##d\theta##/##\pi##
so, ##\rho##(##\theta##) = 1/##\pi##

I couldn't do the b part. So I looked at the solution and it says:
##\rho##(##\theta##)##d\theta## = ##\rho##(x)dx

Why? Can you please tell me the reasoning?
 
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  • #2
Kaguro said:
I couldn't do the b part. So I looked at the solution and it says:
##\rho##(##\theta##)##d\theta## = ##\rho##(x)dx

Why? Can you please tell me the reasoning?

That's the definition of probability density. If we take a small angular range ##(\theta, \theta + d \theta)##, then the probability the needle lands in that range is ##\rho(\theta)d\theta##.

And, if we project that range onto the x-axis, ##(x, x+dx)##, then the probability it lands in that range is ##\rho'(x) dx##

Note I've used ##\rho'## to indicate that it's actually a different function in each case.
 
  • #3
Why does projecting this range conserves probability?
The intervals are different...

Instead of projection I could have taken a completely weird relation like
u = rcos(##\theta##) + 3rsin(##\theta##/2)

Now the total range is r to 2r.

But interval of d##\theta## doesn't correspond to the interval of du... then there's no sense of "probability in this region of space" should be same...
 
  • #4
Kaguro said:
Why does projecting this range conserves probability?
The intervals are different...

Instead of projection I could have taken a completely weird relation like
u = rcos(##\theta##) + 3rsin(##\theta##/2)

Now the total range is r to 2r.

But interval of d##\theta## doesn't correspond to the interval of du... then there's no sense of "probability in this region of space" should be same...

In this case ##x## and ##dx## can be expressed as the appropriate functions of ##\theta##. Probability must be conserved.

E.g. if we had a line at an angle, then there would be a linear relationship between the length along the line and the ##x## coordinate. In this case the mapping is from the semi-circle to the diameter.
 
  • #5
I think that, yes... it should be correct..

The interval is just a matter of system...

The physical reality of probability must not depend upon the system used...

If the interval is different then the density also should be different so as to conserve the probability..
 
  • #6
Kaguro said:
I think that, yes... it should be correct..

The interval is just a matter of system...

The physical reality of probability must not depend upon the system used...

If the interval is different then the density also should be different so as to conserve the probability..

Yes, which is why ##\rho## is a different function in each case.
 
  • #7
It's really a nuissance with this book. Of course you must write ##\tilde{\rho}(x)## since it's NOT the same function. A density by definition transforms as a density! Who'd guessed this.

It's easy to understand. If you have a random number ##\theta## with a probability distribution ##\rho(\theta)## then the meaning is: When measuring ##\theta## the probability to find its value to be in an infinitesimal intervall ##(\theta,\theta+\mathrm{d}\theta)## is given by ##\mathrm{d} P=\mathrm{d} \theta \rho(\theta)##.

Now we have a one-to-one map from ##\theta \in [0,\pi]## to ##x \in[-L,L]## given by ##x=L \cos \theta##, where ##L## is the length of the needle. Since it's a one-to-one mapping the probability to find ##\theta## in the intervall ##(\theta,\theta+\mathrm{d} \theta)## we have
$$\rho(\theta) \mathrm{d} \theta = \tilde{\rho}(x) \mathrm{d} x,$$
from which we get
$$\tilde{\rho}(x)=\rho(\theta) \left |\frac{\mathrm{d} \theta}{\mathrm{d} x} \right|.$$
Now ##\theta=\arccos(x/L)## and thus ##|\mathrm{d} \theta/\mathrm{d} x|=\frac{1}{\sqrt{L^2-x^2}}##. So we finally get
$$\tilde{\rho}(x)=\frac{1}{\pi \sqrt{L^2-x^2}}, \quad x \in [-L,L],$$
because obviously ##\Theta(\theta)=1/\pi##.
 
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1. What is a probability density?

A probability density is a function that describes the likelihood of a continuous random variable taking on a particular value within a given range. It is often represented graphically as a curve and can be used to calculate the probability of a random variable falling within a specific range of values.

2. How is a probability density different from a probability distribution?

A probability distribution is a function that describes the probabilities of all possible outcomes of a random variable. A probability density, on the other hand, is a function that describes the likelihood of a continuous random variable taking on a particular value within a given range. In other words, a probability density is a type of probability distribution that is used for continuous random variables.

3. How is a probability density calculated?

A probability density is calculated by taking the derivative of the cumulative distribution function (CDF) of a continuous random variable. The CDF is a function that gives the probability that the random variable takes on a value less than or equal to a given value. By taking the derivative of the CDF, we can find the probability density function.

4. What is the relationship between probability density and probability?

Probability density and probability are related, but they are not the same thing. Probability density is a function that describes the likelihood of a continuous random variable taking on a particular value within a given range. Probability, on the other hand, is a measure of the likelihood of a specific outcome occurring. Probability can be calculated by integrating the probability density function over a given range of values.

5. How is a probability density used in statistics?

Probability density is used in statistics to help us understand the likelihood of different outcomes for continuous random variables. It is often used in hypothesis testing, where we compare the probability of obtaining a certain result under different conditions. Probability density is also used in calculating confidence intervals and making predictions based on data.

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