Examples of using A random variable in English and their translations into Bulgarian
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I(X) is itself a random variable.
Entropy quantifies the uncertainty involved in predicting the value of a random variable.
So let's say that I have a random variable capital X.
And in this video,I'm going to introduce you to the concept of the expected value of a random variable.
So let's say I have a random variable, X.
Or, since it's a random variable, the expected value of this random variable. .
Now this girl, Keri,she's a random variable.
Let's define a random variable, X, like we always do.
These are just specific instances of a random variable.
For a random variable having probability density p(x), any point at which p(x) has a maximum is said to be a mode.
That sample mean is a random variable.
Under a random variable we understand each real-valued measurable function defined on the elementary random events B.
The mean and standard deviation of a random variable x are -9 and 2 respectively.
And that's how you do it for an expected value of a random variable.
In statistics, a random variable is an assignment of a numerical value to each possible outcome of an event space.
In information theory, entropy is a measure of the uncertainty associated with a random variable.
But if you think about it,well, a random variable really is-- you can kind of view it as each instance of a random variable.
In probability theory and mathematical statistics,one of the characteristics of a distribution of a random variable.
So if we solved all the probabilities that a random variable can take, or we're summing over all of the values, this is going to sum up to 1.
But now, let's prove it to ourselves that this is really true for any a random variable that's described by a binomial distribution.
So the expected value of a random variable, the expected value of a random variable is the exact same thing as the population mean.
Now we know that the expected value,the way you calculate an expected value of a random variable is you just take the probability weighted sum.
So what z is,z is a random variable where you're taking n samples from this distribution up here, this population distribution, taking its mean.
The expected value of negative y-- I will do it over here-- the expected value of the negative of a random variable is just a negative of the expected value of that random variable.
FDIST is calculated as FDIST=P(F>x),where F is a random variable that has an F distribution with deg_freedom1 and deg_freedom2 degrees of freedom.
But what's useful now is we can apply the same principles, but we're finding the arithmetic mean of an infinite population, orthe expected value of a random variable, which is the same thing as the arithmetic mean of the population of this random variable. .
DIST. RT is calculated as F. DIST. RT=P(F>x),where F is a random variable that has an F distribution with deg_freedom1 and deg_freedom2 degrees of freedom.
For example, in the theory of probability andmathematical statistics method used to determine the characteristics of a random variable is the standard deviation, which determines the width of the range of values of the random variable. .
The binomial distribution tells us that the expected value of a random variable is equal to the number of trials that that random variable's kind of composed of, right?
The law of large numbers will just tell us that-- let's say I have a random variable-- X is equal to the number of heads after 100 tosses of a fair coin-- tosses or flips of a fair coin.