What is the Optimal Value of k for a Probability Density Function?

In summary, the conversation discusses a question about finding the value of k for a function to be a probability density. The properties of a probability density function, such as having an area under the curve of 1 and being non-negative for all values of x, are also mentioned. The conversation concludes with the confirmation that k= \frac{1}{2\sqrt{\pi}} is the correct value for the given function to resemble the Normal distribution.
  • #1
theperthvan
184
0
Here is a question that was in on of my exams a few months ago. It asks what is the value of k so that the function f(x;k) is a probability density.
I didn't really answer it, but put an answer as [tex] k= \frac{1}{2\sqrt{\pi}}[/tex] because that somewhat resembled the Normal distribution.
Does anyone know?

[tex]f(x;k) = -(\frac{1}{2\sqrt{\pi}} - k)^2 + k.e^{(10x - 0.25x^2 - 100)}[/tex]

(this is not a homework question)
 
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  • #2
What are the properties of a probability density function?
 
  • #3
Area under curve = 1
 
  • #4
theperthvan said:
Area under curve = 1

Right, i.e. [tex]\int_{-\infty}^{+\infty} f(x)dx =1[/tex]
 
  • #5
You also need f(x)>=0 for all x for f(x) to be a probability density.
 
  • #6
But [tex]k.e^{(10x - 0.25x^2 - 100)}[/tex] isn't able to be integrated using elementary functions.
 
  • #7
theperthvan said:
But [tex]k.e^{(10x - 0.25x^2 - 100)}[/tex] isn't able to be integrated using elementary functions.

That doesn't matter, does it? The standard normal distribution integrates to 1:

[tex]\int_{-\infty}^{\infty} \frac 1 {\sqrt{2\pi}} \exp\left(-\,\frac1 2 x^2\right) = 1[/tex]

From this, you should be able to calculate the integral of [itex]k.e^{(10x - 0.25x^2 - 100)}[/itex] over all x.

BTW, your guess was right because setting [itex] k= \frac{1}{2\sqrt{\pi}}[/itex] eliminates the first term and makes the latter term a normal PDF. Can you see why?
 
  • #8
Ahh, right. I guessed it for that reason (to eliminate the other term), but kind of by accident.

I see how it goes now. Thanks for your responses.
 

FAQ: What is the Optimal Value of k for a Probability Density Function?

What is a probability distribution?

A probability distribution is a mathematical function that describes the likelihood of all possible outcomes of a random variable. It shows the probabilities of each outcome occurring, allowing us to make predictions about the likelihood of certain events happening.

What is the difference between a discrete and continuous probability distribution?

A discrete probability distribution is one where the possible outcomes are countable and distinct, such as rolling a dice. A continuous probability distribution is one where the possible outcomes are uncountable and can take on any value within a range, such as the height of a person.

What is the area under a probability distribution curve?

The area under a probability distribution curve represents the total probability of all possible outcomes. It is always equal to 1 for a valid probability distribution, as the sum of all probabilities must equal 1. The area under the curve also represents the likelihood of a random variable falling within a certain range of values.

How do you calculate the mean of a probability distribution?

The mean of a probability distribution is calculated by multiplying each possible outcome by its corresponding probability and then summing all the values. It is also known as the expected value and represents the average value of a random variable over a large number of trials.

What is the central limit theorem and how does it relate to probability distributions?

The central limit theorem states that when independent random variables are added, their sums will tend towards a normal distribution, regardless of the distribution of the individual variables. This is important because many real-world phenomena can be modeled using normal distributions, making the central limit theorem a powerful tool in statistics and probability.

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