Software Engineer from Quito, Ecuador.
In the simulation of Markov Chain on continuous time, the main idea is to pick the right time of the simulation in which transitions occur exactly at the moments in which changes on the system happen. Let's consider the following example. There are c...
Introduction In statistics and simulation, a Poisson process is stochastic of continuous-time that generally consists on “counting” the events as they occur over a period of time, as this events occur continuously and are independent to one another. ...
In a Wiener process w(t): If the time intervals [s, t] and [s', t'] do not intersect, then the increments w(t) - w(s) and w(t') - w(s') are independent random variables. As the increments w(t) - w(s) are normal random variables with E{w(t) - w(s)} =...
In a system that can change its state, if we define the possible states as 1, 2, 3, ... , n. The states as X1, X2, X3, ... , Xn. The changes on the states will happen on discrete instants of time t1, t2, t3, ... This process of change in the system ...
Applying the chi-square test for the case of discrete random variables with an infinite number of values (m = ∞). In this case the distribution can be geometric or poisson's, an infinite number of intervals are used by grouping some of the values. Th...
The Poisson distribution represents the probability of the distribution of a number of events occurred in fixed interval of time, if this events happen independently and with a fixed constant mean defined by λ. The values of that random variable X = ...
