mcq measures of central tendency with correct
... When all the values in a series occur the equal number of times, then it is not possible to calculate the:
(a) Arithmetic mean
(b) Geometric mean
(c) Harmonic mean
(d) Weighted mean
MCQ No 3.35
The mean for a set of data obtained by assigning each data value a weight that reflects its relative
... It is ironic that the first thing to note about random numbers is how to make them reproducible.
Before using a random-number function, type
set seed #
where # is any integer between 0 and 231 − 1, inclusive, to draw the same sequence of random
numbers. It does not matter which integer you choose as ...
Swarm Intelligence based Soft Computing Techniques for the
... solutions that are non-dominated with respect to each
other. This set is known as Pareto optimal set. While
moving from one Pareto solution to another, there is
always a certain amount of sacrifice in one or more
objectives to achieve a certain amount of gain in the
other(s). Pareto optimal solution ...
probability and stochastic processes
... generation of a single forefather. The eventual goal will be to determine the probability of
the ultimate extinction of all family lines of the said forefather. This problem will be solved
much later in Lecture 3. In this lecture, we confine ourselves in analyzing the mean and
variance of the distri ...
Lesson 9: The Mean Absolute Deviation (MAD)
... in a data distribution called the mean absolute deviation, denoted by MAD. The concept of deviation from the mean
should be clear to students by now, since previous lessons used deviations to develop the idea of the mean as a balance
point. This lesson challenges students to answer why absolute ...
Mean field particle methods
Mean field particle methods are a broad class of interacting type Monte Carlo algorithms for simulating from a sequence of probability distributions satisfying a nonlinear evolution equationThese flows of probability measures can always be interpreted as the distributions of the random states of a Markov process whose transition probabilities depends on the distributions of the current random states. A natural way to simulate these sophisticated nonlinear Markov processes is to sample a large number of copies of the process, replacing in the evolution equation the unknown distributions of the random states by the sampled empirical measures. In contrast with traditional Monte Carlo and Markov chain Monte Carlo methodologies these mean field particle techniques rely on sequential interacting samples. The terminologymean field reflects the fact that each of the samples (a.k.a. particles, individuals, walkers, agents, creatures, or phenotypes) interacts with the empirical measures of the process. When the size of the system tends to infinity, these random empirical measures converge to the deterministic distribution of the random states of the nonlinear Markov chain, so that the statistical interaction between particles vanishes. In other words, starting with a chaotic configuration based on independent copies of initial state of the nonlinear Markov chain model,the chaos propagates at any time horizon as the size the system tends to infinity; that is, finite blocks of particles reduces to independent copies of the nonlinear Markov process. This result is called the propagation of chaos property. The terminology ""propagation of chaos"" originated with the work of Mark Kac in 1976 on a colliding mean field kinetic gas model