Most probable value given observation

In summary: At this point, are there properties for conditional expectation involving independent random variables to answer this? I tried to find some but wasn't able to. Any help is appreciated.There are a few properties you could look at. One is the maximization of the likelihood function, which would be achieved when ##c## is equal to 1. Another is the maximization of the product of the two probabilities, which would be achieved when ##c = 2##.
  • #1
Karnage1993
133
1
Suppose I have observed ##Z = 3##, where ##Z = X + Y##, where ##X \sim N(0,9), Y \sim N(0,4)##. How would I find the most probable value of ##X## that would have given me ##Z = 3##?

My attempt at a solution: I was given that ##X## and ##Y## are independent, so that means ##Z \sim N(0+0, 9+4) = N(0,13)##. To find the most probable value of ##X##, we would have to find the highest possible probability of ##Z##. But ##Z## is not discrete, so every probability at each point is 0, which means the highest probability of ##Z## is 0. Not sure what I would do after this, so I took a different direction.

Now I'm trying to find ##E(X|Z=3)## because I would think the mean is the best measure for the "most probable value of X". We have,

##E(X|Z=3) = E(X|X+Y=3)##. At this point, are there properties for conditional expectation involving independent random variables to answer this? I tried to find some but wasn't able to. Any help is appreciated.
 
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  • #2
Karnage1993 said:
But Z is not discrete, so every probability at each point is 0
Yes, but you can still look at the probability density function.

The expectation value does not have to be the most probable value.
 
  • #3
My first instinct is to say that the most likely value of y is 0, so x, having larger standard deviation would be more likely to be 3. However, ##p(x=3 \cap y=0) =.033##. Slightly higher values of y will likely optimize this function.
in general p(xy)=p(x)*p(y). The probability of a discrete value is not zero.
upload_2014-10-9_21-23-5.png
 
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  • #4
RUber said:
Slightly higher values of y will likely optimize this function.
Good catch!
The probability of a discrete value is not zero.
I think it's better to say that the probability density function is not zero. The probability of any single exact number is zero.
 
  • #6
Karnage1993 said:
Suppose I have observed ##Z = 3##, where ##Z = X + Y##, where ##X \sim N(0,9), Y \sim N(0,4)##. How would I find the most probable value of ##X## that would have given me ##Z = 3##?

My attempt at a solution: I was given that ##X## and ##Y## are independent, so that means ##Z \sim N(0+0, 9+4) = N(0,13)##. To find the most probable value of ##X##, we would have to find the highest possible probability of ##Z##. But ##Z## is not discrete, so every probability at each point is 0, which means the highest probability of ##Z## is 0. Not sure what I would do after this, so I took a different direction.

Now I'm trying to find ##E(X|Z=3)## because I would think the mean is the best measure for the "most probable value of X". We have,

##E(X|Z=3) = E(X|X+Y=3)##. At this point, are there properties for conditional expectation involving independent random variables to answer this? I tried to find some but wasn't able to. Any help is appreciated.

Look at
[tex] f(x|z) \equiv \lim_{\Delta x \to 0, \Delta z \to 0} P(x < X < x + \Delta x\,| z < Z < z + \Delta z)\\
= \lim_{\Delta x \to 0, \Delta z \to 0} \frac{P(x < X < x + \Delta x \: \cap \: z < Z < z + \Delta z)}{P(z < Z < z + \Delta z)} [/tex]
For ##z = 3## this will give you
[tex] c \, f_X(x) f_Y(3-x) [/tex]
where ##c## is a normalization constant. For what value of ##x## would that be maximized?
 

1. What is the meaning of "most probable value given observation"?

"Most probable value given observation" refers to the estimated or predicted value of a variable based on the observation of other related variables. It is the value that is most likely to occur based on the available data.

2. How is the most probable value given observation calculated?

The most probable value given observation is calculated using statistical methods such as regression analysis or maximum likelihood estimation. These methods use the available data to determine the relationship between variables and make a prediction for the most probable value.

3. What is the significance of knowing the most probable value given observation?

Knowing the most probable value given observation can help in making informed decisions and predictions. It can also provide insights into the relationship between variables and help in identifying any patterns or trends in the data.

4. Can the most probable value given observation change over time?

Yes, the most probable value given observation can change over time as new data is collected. As more information becomes available, the estimated value can be updated to reflect any changes in the relationship between variables.

5. How accurate is the most probable value given observation?

The accuracy of the most probable value given observation depends on the quality and quantity of the available data, as well as the statistical methods used to calculate it. Generally, the more data and reliable methods used, the more accurate the estimated value will be.

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