Why Must the Endpoints of PDF Functions Match at Boundaries?

In summary, the continuous random variable X is defined in the interval 0 to 4 with two functions, one for 0<=x<=3 and another for 3<x<=4, both with constants a and b. The area under the pdf is 1 and by integrating both functions and setting them equal at x=3, we can solve for a and b. It is necessary for a pdf to be continuous and have properties of f(x)>=0 and an area of 1. The function P(X>x) is essentially the pdf, represented as f(x) in the original question.
  • #1
thereddevils
438
0
Continuous random variable X is defined in the interval 0 to 4, with

P(X>x)= 1- ax , 0<=x<=3

= b - 1/2 x , 3<x<=4

with a and b as constants. Find a and b.

So the area under the pdf is 1, then i integrated both functions and set up my first equation.

Next, it seems that the endpoints of the functions are equal at x=3. Why is it so? Must a pdf be continuous? I thought its properties are only f(x)>=0 and the area under it is 1.
 
Physics news on Phys.org
  • #2
thereddevils said:
Continuous random variable X is defined in the interval 0 to 4, with

P(X>x)= 1- ax , 0<=x<=3

= b - 1/2 x , 3<x<=4

with a and b as constants. Find a and b.

So the area under the pdf is 1, then i integrated both functions and set up my first equation.

Next, it seems that the endpoints of the functions are equal at x=3. Why is it so? Must a pdf be continuous? I thought its properties are only f(x)>=0 and the area under it is 1.

Umm, how does P(X>x) define a pdf?
 
  • #3
bpet said:
Umm, how does P(X>x) define a pdf?

It's as written in the original question but nevermind, take it as f(x).
 

FAQ: Why Must the Endpoints of PDF Functions Match at Boundaries?

What is a probability density function?

A probability density function (PDF) is a mathematical function that describes the likelihood of a random variable taking on a certain value within a given range. It is used to calculate the probability of a continuous variable falling within a certain range of values.

How is a probability density function different from a probability function?

A probability density function is used for continuous random variables, while a probability function is used for discrete random variables. A PDF gives the density of probabilities at a specific point, while a probability function gives the probability of a specific outcome occurring.

What is the integral of a probability density function?

The integral of a probability density function over a certain range gives the probability of the random variable falling within that range. In other words, the area under the curve of a PDF represents the probability of a particular outcome occurring.

How is the shape of a probability density function determined?

The shape of a probability density function is determined by the underlying distribution of the random variable. For example, a normal distribution will have a bell-shaped curve, while a uniform distribution will have a flat, rectangular curve.

Can a probability density function have negative values?

No, a probability density function cannot have negative values. The total probability of all possible outcomes must equal 1, and negative probabilities would make this impossible. However, the function can approach 0 as the value of the random variable approaches negative infinity.

Back
Top