The strong law of large numbers states that with probability 1, the successive arithmetic averages of a sequence of independent and identically distributed random variables converge to their common mean μ . What do the successive geometric averages converge to? That is, what is lim n → ∞ ( ∏ i = 1 n X i ) 1 n
The strong law of large numbers states that with probability 1, the successive arithmetic averages of a sequence of independent and identically distributed random variables converge to their common mean μ . What do the successive geometric averages converge to? That is, what is lim n → ∞ ( ∏ i = 1 n X i ) 1 n
Solution Summary: The author explains how successive geometric averages converge to undersetmathrmnto...
The strong law of large numbers states that with probability 1, the successive arithmetic averages of a sequence of independent and identically distributed random variables converge to their common mean
μ
. What do the successive geometric averages converge to? That is, what is
lim
n
→
∞
(
∏
i
=
1
n
X
i
)
1
n
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, probability and related others by exploring similar questions and additional content below.
Continuous Probability Distributions - Basic Introduction; Author: The Organic Chemistry Tutor;https://www.youtube.com/watch?v=QxqxdQ_g2uw;License: Standard YouTube License, CC-BY
Probability Density Function (p.d.f.) Finding k (Part 1) | ExamSolutions; Author: ExamSolutions;https://www.youtube.com/watch?v=RsuS2ehsTDM;License: Standard YouTube License, CC-BY
Find the value of k so that the Function is a Probability Density Function; Author: The Math Sorcerer;https://www.youtube.com/watch?v=QqoCZWrVnbA;License: Standard Youtube License