Solving Asymetric Probability Distribution w/68% Interval

In summary, the individual is trying to find the shortest interval that encloses 68% of probability in an asymetric probability distribution function using Mathematica. Since there is no analytic solution, they suggest using a search technique by generating a cumulative distribution function and increasing the interval points until the desired probability is enclosed. They also mention the need for additional conditions to define a unique solution.
  • #1
gaby287
14
0
I have an asymetric probability distribution function (pdf), in this case we know that the concept of an error bar does not seem appropriate. Well I'm finding the shortest interval that enclosed the 68% of probability. My problem is that my pdf couldn't be integrated analytically and I'm using Mathematica but I don't know how to find the intervals.
 
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  • #2
I'm not familiar with mathematica, but here are my two cents:
Since there is no analytic way to solve it, a search technique will have to be used. Here is one simple-minded approach.
1) Generate the cumulative distribution function (use EmpiricalDistribution?)
2) Start with the lower interval point at the lower limit of the distribution (within reason) and with the upper interval point one step above the lower. Keep increasing the upper interval point till you get 68% in between lower and upper. Pick a step size that will give you the accuracy you want. If you want to get fancy, you may be able to get an answer with a large step size and then reduce the size to refine your answer.
3) Increase the lower point up one step and increase the upper point (if necessary) step by step till you again get 68% in between lower and upper. If it is a shorter interval, record it.
4) Keep repeating step 3 till the upper interval point hits the upper limit of the distribution (within reason).
5) The final recorded shortest interval is your answer.
 
  • #3
gaby287 said:
. Well I'm finding the shortest interval that enclosed the 68% of probability..

Saying the "shortest" interval doesn't specify a unique interval. For a given length, there can be two or more different intervals that have the same probability. You need to add other conditions if you want to define a unique solution.
 

FAQ: Solving Asymetric Probability Distribution w/68% Interval

What is an asymmetric probability distribution?

An asymmetric probability distribution is a type of probability distribution where the data is not evenly distributed around the mean. This means that the distribution is skewed, with the majority of the data falling on one side of the mean.

Why is it important to solve for the 68% interval in an asymmetric probability distribution?

The 68% interval, also known as the 68% confidence interval, is a range of values that is likely to contain the true population mean. It is important to solve for this interval in an asymmetric probability distribution because it allows us to estimate the population mean and understand the uncertainty around that estimate.

How do you solve for the 68% interval in an asymmetric probability distribution?

To solve for the 68% interval, you first need to calculate the mean and standard deviation of the data. Then, you can use the formula mean ± (1 standard deviation) to find the range of values that fall within the 68% interval. This means that 68% of the data falls within this range, with 34% on either side of the mean.

What causes an asymmetric probability distribution?

An asymmetric probability distribution can be caused by a variety of factors, such as outliers in the data, a skewed underlying distribution, or a biased sample. It is important to identify the cause of the asymmetry in order to accurately interpret the data.

How can an asymmetric probability distribution be used in scientific research?

An asymmetric probability distribution can be used in scientific research to understand the variability and uncertainty in data. It can also be used to make predictions and estimate the likelihood of certain outcomes. Additionally, it can provide insights into the underlying distribution of a population and help identify potential outliers or sources of bias in the data.

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