Answer:
11,000
Step-by-step explanation:
3200+7200+600=11000
The value of 
is 31
Explanation:
The given expression is 
We need to determine the value of
The value of can be determined by substituting 
in the expression
Thus, we have,
Adding the terms, we have,
Thus, the value is 31.
Therefore, the simplified value of the expression by substituting in the expression is 31.
A complex mathematical topic, the asymptotic behavior of sequences of random variables, or the behavior of indefinitely long sequences of random variables, has significant ramifications for the statistical analysis of data from large samples.
The asymptotic behavior of the sample estimators of the eigenvalues and eigenvectors of covariance matrices is examined in this claim. This work focuses on limited sample size scenarios where the number of accessible observations is comparable in magnitude to the observation dimension rather than usual high sample-size asymptotic .
Under the presumption that both the sample size and the observation dimension go to infinity while their quotient converges to a positive value, the asymptotic behavior of the conventional sample estimates is examined using methods from random matrix theory.
Closed form asymptotic expressions of these estimators are obtained, demonstrating the inconsistency of the conventional sample estimators in these asymptotic conditions, assuming that an asymptotic eigenvalue splitting condition is satisfied.
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Let's say the prisms dimensions were 2×2×2 and you doubled the height 4×2×2.
2×2×2=8 while 4×2×2=16
another example:
4×3×6=144 while 8×3×6=288
therefor it doubles the volume of the prism
Answer: Mathematically Bayes’ theorem is defined as
P(A\B)=P(B\A) ×P(A)
P(B)
Bayes theorem is defined as where A and B are events, P(A|B) is the conditional probability that event A occurs given that event B has already occurred (P(B|A) has the same meaning but with the roles of A and B reversed) and P(A) and P(B) are the marginal probabilities of event A and event B occurring respectively.
Step-by-step explanation: for example, picking a card from a pack of traditional playing cards. There are 52 cards in the pack, 26 of them are red and 26 are black. What is the probability of the card being a 4 given that we know the card is red?
To convert this into the math symbols that we see above we can say that event A is the event that the card picked is a 4 and event B is the card being red. Hence, P(A|B) in the equation above is P(4|red) in our example, and this is what we want to calculate. We previously worked out that this probability is equal to 1/13 (there 26 red cards and 2 of those are 4's) but let’s calculate this using Bayes’ theorem.
We need to find the probabilities for the terms on the right-hand side. They are:
P(B|A) = P(red|4) = 1/2
P(A) = P(4) = 4/52 = 1/13
P(B) = P(red) = 1/2
When we substitute these numbers into the equation for Bayes’ theorem above we get 1/13, which is the answer that we were expecting.
