Monte Carlo sampling of solutions to inverse problems
... of the model space by using a method described by Wiggins [1969, 1972] in which the model space was sampled according to the prior distribution ρ(m). This approach is superior to a uniform sampling by crude Monte Carlo. However, the peaks of the prior distribution are typically much less pronounced ...
... of the model space by using a method described by Wiggins [1969, 1972] in which the model space was sampled according to the prior distribution ρ(m). This approach is superior to a uniform sampling by crude Monte Carlo. However, the peaks of the prior distribution are typically much less pronounced ...
spectral properties of trinomial trees
... identically distributed random variables to the density of the limiting variable, known as local limit theorems, have been developed in mathematical statistics (see for example (Kolassa 2006) and chapter XVI in (Feller 1971)). The proofs are based on Edgeworth expansions which use Hermite polynomial ...
... identically distributed random variables to the density of the limiting variable, known as local limit theorems, have been developed in mathematical statistics (see for example (Kolassa 2006) and chapter XVI in (Feller 1971)). The proofs are based on Edgeworth expansions which use Hermite polynomial ...
A sharp threshold in proof complexity yields lower bounds for
... formulas with ð1 eÞn 2-clauses and Dn 3-clauses, for some arbitrary constants D; e40: (For simplicity, we focus on k ¼ 3; extensions to k43 are straightforward.) Theorem 1.1. For every D; e40; if F BFne;D ; then w.h.p. resðF Þ ¼ 2OðnÞ and DPLLðF Þ ¼ 2OðnÞ : Theorem 1.1 represents a sharp threshold ...
... formulas with ð1 eÞn 2-clauses and Dn 3-clauses, for some arbitrary constants D; e40: (For simplicity, we focus on k ¼ 3; extensions to k43 are straightforward.) Theorem 1.1. For every D; e40; if F BFne;D ; then w.h.p. resðF Þ ¼ 2OðnÞ and DPLLðF Þ ¼ 2OðnÞ : Theorem 1.1 represents a sharp threshold ...
Lectures on Elementary Probability
... of trials and M is a population, then a function from I to M is called a ordered sample with replacement of size n from M . If I is a set of balls and M is a set of urns, then a function from I to M is a way of placing the balls in the urns. If I is a set and M is an set with m elements, then a func ...
... of trials and M is a population, then a function from I to M is called a ordered sample with replacement of size n from M . If I is a set of balls and M is a set of urns, then a function from I to M is a way of placing the balls in the urns. If I is a set and M is an set with m elements, then a func ...
Randomness
Randomness is the lack of pattern or predictability in events. A random sequence of events, symbols or steps has no order and does not follow an intelligible pattern or combination. Individual random events are by definition unpredictable, but in many cases the frequency of different outcomes over a large number of events (or ""trials"") is predictable. For example, when throwing two dice, the outcome of any particular roll is unpredictable, but a sum of 7 will occur twice as often as 4. In this view, randomness is a measure of uncertainty of an outcome, rather than haphazardness, and applies to concepts of chance, probability, and information entropy.The fields of mathematics, probability, and statistics use formal definitions of randomness. In statistics, a random variable is an assignment of a numerical value to each possible outcome of an event space. This association facilitates the identification and the calculation of probabilities of the events. Random variables can appear in random sequences. A random process is a sequence of random variables whose outcomes do not follow a deterministic pattern, but follow an evolution described by probability distributions. These and other constructs are extremely useful in probability theory and the various applications of randomness.Randomness is most often used in statistics to signify well-defined statistical properties. Monte Carlo methods, which rely on random input (such as from random number generators or pseudorandom number generators), are important techniques in science, as, for instance, in computational science. By analogy, quasi-Monte Carlo methods use quasirandom number generators.Random selection is a method of selecting items (often called units) from a population where the probability of choosing a specific item is the proportion of those items in the population. For example, with a bowl containing just 10 red marbles and 90 blue marbles, a random selection mechanism would choose a red marble with probability 1/10. Note that a random selection mechanism that selected 10 marbles from this bowl would not necessarily result in 1 red and 9 blue. In situations where a population consists of items that are distinguishable, a random selection mechanism requires equal probabilities for any item to be chosen. That is, if the selection process is such that each member of a population, of say research subjects, has the same probability of being chosen then we can say the selection process is random.