Search results
Results from the WOW.Com Content Network
The Viterbi algorithm is a dynamic programming algorithm for obtaining the maximum a posteriori probability estimate of the most likely sequence of hidden states—called the Viterbi path—that results in a sequence of observed events.
The principle of maximum caliber (MaxCal) or maximum path entropy principle, suggested by E. T. Jaynes, [1] can be considered as a generalization of the principle of maximum entropy. It postulates that the most unbiased probability distribution of paths is the one that maximizes their Shannon entropy. This entropy of paths is sometimes called ...
4.8×10 −2: Probability of being dealt a two pair in poker 10 −1: Deci-(d) 1.6×10 −1: Gaussian distribution: probability of a value being more than 1 standard deviation from the mean on a specific side [20] 1.7×10 −1: Chance of rolling a '6' on a six-sided die: 4.2×10 −1: Probability of being dealt only one pair in poker 5.0×10 −1
Graphs of probability P of not observing independent events each of probability p after n Bernoulli trials vs np for various p.Three examples are shown: Blue curve: Throwing a 6-sided die 6 times gives a 33.5% chance that 6 (or any other given number) never turns up; it can be observed that as n increases, the probability of a 1/n-chance event never appearing after n tries rapidly converges to 0.
Maximal entropy random walk (MERW) is a popular type of biased random walk on a graph, in which transition probabilities are chosen accordingly to the principle of maximum entropy, which says that the probability distribution which best represents the current state of knowledge is the one with largest entropy.
If we take the frequentist view of probability, we believe there is a true probability distribution that generated the data. Then it turns out that the Dirichlet process is consistent in the weak topology , which means that for every weak neighbourhood U {\displaystyle U} of P 0 {\displaystyle P_{0}} , the posterior probability of U ...
Maximum entropy; Soft configuration ... and ask for the probability P that there is a path from the top boundary to the ... 1 − p − 2p 3-4p 4-4p 5 +15 6 + 13p 7 ...
If X n converges in probability to X, and if P(| X n | ≤ b) = 1 for all n and some b, then X n converges in rth mean to X for all r ≥ 1. In other words, if X n converges in probability to X and all random variables X n are almost surely bounded above and below, then X n converges to X also in any rth mean. [10] Almost sure representation ...