Calculate the joint CDF of two random variables

In summary, the conversation discusses finding the joint CDF for the variables ##Z## and ##Y## using a change-of-variables technique. The integration region for ##f_{ZY}## is determined to be ##0<z<(1-y)^2, 0<y<1## and the final integral is given as $$F_{ZY}=\int_0^y\int^x_{(1-y)^2}\frac{1}{2\sqrt{z}}dzdy$$. There is some discussion about the need for a change of variables and the complications that can arise from it.
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$$f_{XY}=1$$
$$dzdy=2xdxdy⇒\frac{1}{2\sqrt{z}}dzdy=dxdy$$
$$f_{ZY}=\frac{1}{2\sqrt{z}}\quad \text{on some region S}$$
$$F_{ZY}=\int^y_{g}\int^x_{h}\frac{1}{2\sqrt{z}}dzdy\quad\text{for some}\quad g(x,y),h(x,y)$$
im learning how to find the region S using a change-of variables technique
 
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The integration region for the joint CDF ##f_{ZY}## is always ##z<a## and ##y<b## by definition.

However, there is no real need to make the change of variables here in the first place. You can just integrate in the xy variables. (This is where you need to be careful with your integration region!)

Edit: Another complication in the change of variables is that your change of variables is not a bijection. There are two x values corresponding to the same z value for all ##x \neq 0##.
 
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because i get confused easily, i shamelessly copied the change of variables technique on this link i found on Goggle.

$$f_{ZY}=\frac{1}{2\sqrt{z}}\cdot f_{XY}\quad \text{on}\quad 0<z<(1-y)^2, 0<y<1$$

where ##\frac{1}{2\sqrt{z}}## is the absolute value of the Jacobian determinant and ##f_{XY}=1##. the region was determined using the inverse transforms ##x=\sqrt{z}## and ##y=y##.

is this okay to find the joint CDF by integrating over a square although the region S is not a square? $$F_{ZY}=\int_0^x\int_0^y\frac{1}{2\sqrt{z}}dzdy$$

or is it more like this because I'm supposed to include the boundary information somewhere in the integral?

$$F_{ZY}=\int_0^y\int^x_{(1-y)^2}\frac{1}{2\sqrt{z}}dzdy$$
 
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FAQ: Calculate the joint CDF of two random variables

What is a joint CDF?

A joint CDF (cumulative distribution function) is a mathematical function that describes the probability of two or more random variables taking on certain values or falling within certain ranges. It is used to calculate the probability of events involving multiple random variables.

How is the joint CDF calculated?

The joint CDF is calculated by taking the integral of the joint probability density function (PDF) over a certain range of values. This integrates the probability density function over all possible values and gives the cumulative probability of the random variables falling within that range.

What is the difference between a joint CDF and a marginal CDF?

A joint CDF describes the probability of multiple random variables, while a marginal CDF describes the probability of a single random variable. The marginal CDF is obtained by fixing the values of all other random variables in the joint CDF and integrating over the remaining variable.

Can the joint CDF be used to calculate the probability of specific events?

Yes, the joint CDF can be used to calculate the probability of events involving multiple random variables. By setting the ranges of the random variables in the joint CDF, the probability of the event falling within that range can be calculated.

What is the relationship between the joint CDF and the joint PDF?

The joint CDF is the integral of the joint PDF, which describes the probability of the random variables taking on specific values. The joint CDF gives the cumulative probability of the random variables falling within certain ranges, while the joint PDF gives the probability of the random variables taking on specific values.

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