Triple

Target class: refinement of the law of large numbers
Context triple: [Erdős–Rényi law of large numbers, instanceOf, refinement of the law of large numbers]

• A. quantitative central limit theorem
  The quantitative central limit theorem provides explicit bounds on how quickly the distribution of normalized sums of random variables converges to the normal distribution, typically in terms of metrics like the Kolmogorov or Wasserstein distance.
• B. central limit theorem
  The central limit theorem states that, under broad conditions, the sum (or average) of a large number of independent, identically distributed random variables tends to follow a normal distribution, regardless of the original variables’ distribution.
• C. tool in large deviation theory
  A tool in large deviation theory is a mathematical method or result—such as rate functions, the Gartner–Ellis theorem, or contraction principles—used to quantify and analyze the exponentially small probabilities of rare events in stochastic systems.
• D. result in probability theory chosen
  In probability theory, a result is a formally stated and proven fact—such as a theorem, lemma, or corollary—that describes a property or relationship involving probabilistic concepts like random variables, events, or distributions.
• E. object in optimal stopping theory
  An object in optimal stopping theory is an abstract entity (such as a stochastic process, payoff function, or stopping rule) whose evolution or evaluation over time determines when it is best to stop observing and take an action to maximize expected reward or minimize expected cost.
• F. None of above.