Exponential distribution question

In summary, the conversation discusses the concept of probability and probability density in the context of the exponential distribution PDF. The speaker questions how it is possible for the probability to be almost 1 for a very small value of X, and the responder explains that this is due to the graph being a probability density graph, which means the density can be very large for a small interval of X values. However, the actual probability itself never exceeds 1.
  • #1
oneamp
219
0
Hi. I notice that some values of X on the exponential distribution PDF have a value of around 1. I understand the integral ends up being one, since those values of X are less than 1. But P(X) at those points still gets to 1, or thereabouts. How does that make sense, that the probability of a value, say 0.00001, is about 1, and the others complete the integral to 1?

I hope this question makes sense.

Thank you.
 
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  • #2
oneamp said:
the probability of a value, say 0.00001, is about 1,.
Do you mean the probability of 0<X< 0.00001 is almost 1 or that the PDF at x=0.0001 is about 1?

In the first case, the PDF values must be very large for the integral on the interval 0<X<0.00001 to be nearly 1. Then, yes, there is very little probability of higher values of X.

In the second case the integral over 0<X<0.00001 is about 0.00001. That leaves a lot of probability (0.9999) for higher values of X.
 
  • #3
Here's an example of what I'm talking about, from http://www.itl.nist.gov/div898/handbook/eda/section3/eda3668.htm.

rhpis6.jpg


The probability in the top left graph actually goes above 1 for some values of X < 1. How is this even possible?

Thank you
 
  • #4
Imagine a rectangle that is very tall. Say it's 10, 000 meters tall. Does that mean it has a big area?

Well, what if its width is only 0.0000000000000000000000000000000000000000000000001 meters?

Obviously, its area is extremely small, even though it's very tall. If you multiply that tiny number by 10, 000 to get the area, it's still ridiculously small. For essentially the same reason, it's quite possible for a graph to be much taller than 1 for a while, while still having a total area of 1.
 
  • #5
Thank you
 
  • #6
oneamp said:
Here's an example of what I'm talking about, from http://www.itl.nist.gov/div898/handbook/eda/section3/eda3668.htm.

rhpis6.jpg


The probability in the top left graph actually goes above 1 for some values of X < 1. How is this even possible?

Thank you

Remember that those are probability density graphs, not probability graphs. t
The densities are per unit of X. so they can get very large for a short interval of X values. The probabilities themselves never get over 1.
 
  • #7
Thank you
 

Related to Exponential distribution question

1. What is an exponential distribution?

An exponential distribution is a probability distribution that describes the time between events in a Poisson process. It is often used to model events that occur randomly and independently over a continuous period of time.

2. How is an exponential distribution different from other distributions?

An exponential distribution is unique in that it has a constant hazard rate, meaning that the probability of an event occurring in a given time interval is always the same regardless of how much time has passed. Other distributions, such as the normal distribution, do not have this property.

3. What are some real-life applications of the exponential distribution?

The exponential distribution is commonly used in reliability and survival analysis, as it can be used to model the time between failures or deaths. It is also used in queuing theory to model the arrival and service times of customers in a system. Additionally, it has applications in finance, biology, and physics.

4. How is the exponential distribution related to the Poisson distribution?

The Poisson distribution is a discrete probability distribution that describes the number of events occurring in a fixed time interval. The exponential distribution is often used to model the time between these events in a Poisson process. In fact, if the time between events follows an exponential distribution, then the number of events in a fixed time interval will follow a Poisson distribution.

5. How is the exponential distribution parameterized?

The exponential distribution is parameterized by a single parameter, λ (lambda), which represents the rate at which events occur. This parameter is equal to the inverse of the mean of the distribution. The larger the value of λ, the more frequent the events occur.

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