A binomial distribution with parameters n and p is used to model the number of non-conforming items in a sequence of n trials for an attribute quality characteristic, in which p denotes the process fraction non-conforming.
In the case of multi-attribute processes, the multivariate binomial distribution with parameters n and p k (k = 1, 2,…, q) can be used to model the number of non-conforming items in a sequence of n trials for each attribute quality characteristic, in which p k denotes the process fraction non-conforming of the k th quality characteristic.
Development of oncologic therapies has traditionally been performed in a sequence of clinical trials intended to assess safety (phase I), preliminary efficacy (phase II), and improvement over the standard of care (phase III) in homogeneous (in terms of tumor type and disease stage) patient populations.
Γ(n)=(n−1)! denotes the Gamma function, and y represents the number of failures which occur in a sequence of Bernoulli trials before a target number of successes is reached.
Classical conditioning can be modeled in one of two ways: as a sequence of separate trials, in which the agent is restarted in a set <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0007362.e266.PNG" class= inline-graphic"/> state each time or as a loop with an identifiable inter-trial-interval (ITI) state [5], [8], [14], [24].
Each symbol shows the average learned eye velocity on a given trial in a sequence of 100 learning trials, averaged across 10 repetitions of the experiment.
If we consider the history of the interception responses in the course of an experiment, we find evidence of transient after-effects of the moving characters. Figure 5 plots the change of response timing in a sequence of consecutive static trials following a dynamic trial.
It is well known that, in a sequence of Bernoulli (p) trials, if n p→λ as n→∞, then the probability of k successes in n trials can be approximated by a Poisson probability with parameter λ, denoted P.
The distribution of the number of runs and patterns in a sequence of multi-state trials or random permutations of a set of integers have been successfully used in various fields in applied probability, statistics and discrete mathematics.
The binomial distribution is a discrete probability distribution for the number of successes in a sequence of independent Bernoulli trials with probability of success p. The probability mass function for having k s successes in m s trials is begin{aligned} p_{B,m_{s}} k_{s}) = {m_{s} choose k_{s}} p^{k_{s}} 1-p)^{m_{s} 1-ps}}.
As described for the constant reaching task, the foveal reaching task was tested in a sequence of randomized triplets, 15 trials for each condition.
