Markov's inequality can be used with Chebyshev's inequality to derive one form of this law, a form that also leads to the asymptotic equipartition property. The other form of this theorem applies if the sum of all variances divided by the index squared converges; that is known as the Kolmogorov criterion, and applies to the strong version of this theorem, which adds that the convergence is almost sure. With a name coined by Simeon Poisson, identify this law that states that as n approaches infinity, the arithmetic mean of n random variables converges to its expected value.
Markov's inequality can be used with Chebyshev's inequality to derive one form of this law, a form that also leads to the asymptotic equipartition property. The other form of this theorem applies if the sum of all variances divided by the index squared converges; that is known as the Kolmogorov criterion, and applies to the strong version of this theorem, which adds that the convergence is almost sure. With a name coined by Simeon Poisson, identify this law that states that as n approaches infinity, the arithmetic mean of n random variables converges to its expected value.
Markov's inequality can be used with Chebyshev's inequality to derive one form of this law, a form that also leads to the asymptotic equipartition property.
law of {large numbers} [accept “{weak}” or “{strong}” as a {modifier}; accept {Bernoulli's Theorem}]