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American YouTube personality MrBeast is the most-subscribed channel on YouTube, with 346 million subscribers as of January 2025.. A subscriber to a channel on the American video-sharing platform YouTube is a user who has chosen to receive the channel's content by clicking on that channel's "Subscribe" button, and each user's subscription feed consists of videos published by channels to which ...
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 ...
In his first paper on Markov chains, published in 1906, Markov showed that under certain conditions the average outcomes of the Markov chain would converge to a fixed vector of values, so proving a weak law of large numbers without the independence assumption, [16] [17] [18] which had been commonly regarded as a requirement for such ...
The process is Markovian only at the specified jump instants, justifying the name semi-Markov. [1] [2] [3] (See also: hidden semi-Markov model.) A semi-Markov process (defined in the above bullet point) in which all the holding times are exponentially distributed is called a continuous-time Markov chain. In other words, if the inter-arrival ...
Another discrete-time process that may be derived from a continuous-time Markov chain is a δ-skeleton—the (discrete-time) Markov chain formed by observing X(t) at intervals of δ units of time. The random variables X (0), X (δ), X (2δ), ... give the sequence of states visited by the δ-skeleton.
Suppose further that the walk stops if it reaches 0 or m ≥ a; the time at which this first occurs is a stopping time. If it is known that the expected time at which the walk ends is finite (say, from Markov chain theory), the optional stopping theorem predicts that the expected stop position is equal to the initial position a.
Every adapted right continuous Feller process on a filtered probability space (,, ()) satisfies the strong Markov property with respect to the filtration (+), i.e., for each (+)-stopping time, conditioned on the event {<}, we have that for each , + is independent of + given .
First hitting times are central features of many families of stochastic processes, including Poisson processes, Wiener processes, gamma processes, and Markov chains, to name but a few. The state of the stochastic process may represent, for example, the strength of a physical system, the health of an individual, or the financial condition of a ...