We generated four datasets of 200,000 instances drawn from a

Bernoulli distribution. The first 100,000 instances were drawn from stationary

Bernoulli distribution whose mean value was 0.01.

where [[xi].sup.i.sub.1](k) and [xi.sup.i.sub.2](k) obey the

Bernoulli distribution, [mathematical expression not reproducible], and 0 [less than or equal to] [[alpha].sup.i.sub.2] [less than or equal to] 1, i = 1, 2, ..., L, while [[alpha].sup.i.sub.1] represents the probability of fusion center receiving position without time delay.

The

Bernoulli distribution cannot be seen as continuous and violates the normality assumption the most.

Therefore under [H.sub.0] we have that [I.sub.A] and [I.sub.B] follow a

Bernoulli distribution with probability of success [p.sub.A] = 1/2 and [p.sub.B] = 1/2 respectively.

(19) Here is the

Bernoulli distribution for 10 tosses, number of heads along the bottom, number of occurrences from 1024 possibilities on the side: (see figure and table next page)

The

Bernoulli distribution determined by p is the probability distribution of a random configuration c [member of] [S.sup.Z] if the values [c.sub.i], for i [member of] Z, are chosen randomly and independently, each with distribution p.

A question having 'm' multiple choices when awarded marks either 0 or 1, implies that the marks is a random variable 'X' which has a

Bernoulli distribution i.e.

[3] shows that if we have a finite support discrete distribution such as the

Bernoulli distribution, then we can have the elements of the probability vector updated according to

Raghavan and Upfal have given a protocol in which the expected delay (time to get serviced) of every message is O(log n) when messages are generated according to a

Bernoulli distribution with generation rate up to about 1/10.

The

Bernoulli distribution is the appropriate distribution to use when multiple admissions are not possible.