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  2. Optimal stopping - Wikipedia

    en.wikipedia.org/wiki/Optimal_stopping

    A key example of an optimal stopping problem is the secretary problem. Optimal stopping problems can often be written in the form of a Bellman equation , and are therefore often solved using dynamic programming .

  3. Robbins' problem - Wikipedia

    en.wikipedia.org/wiki/Robbins'_problem

    In probability theory, Robbins' problem of optimal stopping [1], named after Herbert Robbins, ... where is the solution to the equation [note 1] = / which ...

  4. Odds algorithm - Wikipedia

    en.wikipedia.org/wiki/Odds_algorithm

    In decision theory, the odds algorithm (or Bruss algorithm) is a mathematical method for computing optimal strategies for a class of problems that belong to the domain of optimal stopping problems. Their solution follows from the odds strategy, and the importance of the odds strategy lies in its optimality, as explained below.

  5. Secretary problem - Wikipedia

    en.wikipedia.org/wiki/Secretary_problem

    The optimal policy for the problem is a stopping rule. Under it, the interviewer rejects the first r − 1 applicants (let applicant M be the best applicant among these r − 1 applicants), and then selects the first subsequent applicant that is better than applicant M. It can be shown that the optimal strategy lies in this class of strategies.

  6. Optional stopping theorem - Wikipedia

    en.wikipedia.org/wiki/Optional_stopping_theorem

    In probability theory, the optional stopping theorem (or sometimes Doob's optional sampling theorem, for American probabilist Joseph Doob) says that, under certain conditions, the expected value of a martingale at a stopping time is equal to its initial expected value. Since martingales can be used to model the wealth of a gambler participating ...

  7. Early stopping - Wikipedia

    en.wikipedia.org/wiki/Early_stopping

    In machine learning, early stopping is a form of regularization used to avoid overfitting when training a model with an iterative method, such as gradient descent. Such methods update the model to make it better fit the training data with each iteration. Up to a point, this improves the model's performance on data outside of the training set (e ...

  8. AOL Mail for Verizon Customers - AOL Help

    help.aol.com/products/aol-mail-verizon

    AOL Mail welcomes Verizon customers to our safe and delightful email experience!

  9. Stopping time - Wikipedia

    en.wikipedia.org/wiki/Stopping_time

    Example of a stopping time: a hitting time of Brownian motion.The process starts at 0 and is stopped as soon as it hits 1. In probability theory, in particular in the study of stochastic processes, a stopping time (also Markov time, Markov moment, optional stopping time or optional time [1]) is a specific type of “random time”: a random variable whose value is interpreted as the time at ...