Probability most probable value

In summary, we are given a discrete random variable ##X## and we define a most probable value for ##X## as the value ##x_0## where the probability mass function ##p_X(x_0)## is maximum. We are asked to show that every discrete random variable has at least one most probable value, and to check that ##[(n+1)p]## is a most probable value for ##X \sim Bi(n,p)## and ##[\lambda]## is a most probable value for ##X \sim Poisson(\lambda)##. We can find the supremum of the binomial distribution by calculating the ratio of consecutive terms in the binomial mass function and determining if it is increasing. Similarly,
  • #1
mahler1
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Homework Statement



Let ##X## be a discrete random variable, we say that ##x_0 \in R_X## is a most probable value for ##X## if

##p_X(x_0)=sup_{x \in R_X} p_X(x)##.

1)Show that every discrete random variable admits at least one most probable value.

2) Check that ##[(n+1)p]## is a most probable value for ##X \sim Bi(n,p)##, and that ##[\lambda]## is a most probable value for the Poisson distribution of parameter ##\lambda##


The Attempt at a Solution



I am pretty lost in both parts of the problem. As for 2), I know that if ##X## has a binomial distribution ##B(n,p)##, then the mass function

##p_X(x)=\binom{n}{x}(1-p)^{n-x}p^x##,

and that if ##X## has a poisson distribution, then the mass function is

##p_X(x)=\dfrac{\lambda^x}{x!}e^{-\lambda}##.

How can I find the supreme of both functions? Any help would be appreciated.
 
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  • #2
mahler1 said:

Homework Statement



Let ##X## be a discrete random variable, we say that ##x_0 \in R_X## is a most probable value for ##X## if

##p_X(x_0)=sup_{x \in R_X} p_X(x)##.

1)Show that every discrete random variable admits at least one most probable value.

2) Check that ##[(n+1)p]## is a most probable value for ##X \sim Bi(n,p)##, and that ##[\lambda]## is a most probable value for the Poisson distribution of parameter ##\lambda##


The Attempt at a Solution



I am pretty lost in both parts of the problem. As for 2), I know that if ##X## has a binomial distribution ##B(n,p)##, then the mass function

##p_X(x)=\binom{n}{x}(1-p)^{n-x}p^x##,

and that if ##X## has a poisson distribution, then the mass function is

##p_X(x)=\dfrac{\lambda^x}{x!}e^{-\lambda}##.

How can I find the supreme of both functions? Any help would be appreciated.

If
[tex] b(k) \equiv b_{n,p}(k) = {n \choose k} p^k (1-p)^{n-k},[/tex]
what is a simple expression for
[tex] r(k) = \frac{b(k+1)}{b(k)}\:?[/tex]
So, how can you tell if ##b## is increasing at ##k## (that is, if ##b(k+1) \geq b(k)##)?

Do something similar for the Poisson distribution.
 
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1. What is the most probable value in probability?

The most probable value in probability refers to the value that is most likely to occur in a set of data. It is calculated using the probability distribution function, which takes into account the likelihood of each possible value occurring.

2. How is the most probable value different from the mean?

The most probable value and the mean are two different measures of central tendency. While the most probable value represents the value that is most likely to occur, the mean represents the average value of the entire dataset. In some cases, the most probable value and the mean may be the same, but in other cases, they may be different.

3. Can the most probable value be greater than the mean?

Yes, the most probable value can be greater than the mean. This can happen when the data is skewed, meaning that it is not evenly distributed around the mean. In this case, the most probable value may be located on the longer tail of the distribution, while the mean is pulled towards the shorter tail.

4. How is the most probable value used in real-life applications?

The most probable value is used in various real-life applications, such as in statistics, economics, and engineering. It can help predict the likelihood of certain events occurring and make informed decisions based on the probability of different outcomes.

5. Is the most probable value the same as the mode?

No, the most probable value and the mode are not the same. The mode is the value that occurs most frequently in a dataset, while the most probable value is the value that is most likely to occur. In some cases, the mode and the most probable value may be the same, but in other cases, they may be different.

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