The probability density functions fitting to the discrete probability functions has always been needed, and very important. This paper is fitting the continuous curves which are probability density functions to the binomial probability functions, negative binomial geometrics, poisson and hypergeometric. The main key in these fittings is the use of the derivative concept and common differential ...

the stochastic outcomes and phenomenon of climate are the reason to use probability sciences in climatology. interactions of most of climatic phenomenon as well as justify them are very acceptable based on probability technique. accordingly it is useful in environmental management and planning. a conditional probability is the probability of an event given that another event has occurred e.g. l...

the probability of losing a customer in m/g/n/0 and gi/m/n/0 loss queuing systems withheterogeneous servers is minimized. the first system uses a queue discipline in which a customer who arriveswhen there are free servers chooses any one of them with equal probability, but is lost otherwise. providedthat the sum of the servers rates are fixed, loss probability in this system attains minimum val...

We compare water quality valuation results from a probability sample and two opt-in non-probability samples, MTurk Qualtrics. The samples differ in some key demographics, but measured attitudes are strikingly similar. For models, most parameters were significantly different across yet many of the marginal willingness to pay similar samples. Notably, for non-marginal changes there differences by...

consider search designs for searching one nonzero 2- or 3-factor interaction under the search linear model. in the noisy case, search probability is given by shirakura et al. (ann. statist. 24(6) (1996) 2560). in this paper some new properties of the searching probability are presented. new properties of the search probability enable us to compare designs, which depend on an unknown parameter ?...

‎let $g$ be a finite group and $d_2(g)$ denotes the probability ‎that $[x,y,y]=1$ for randomly chosen elements $x,y$ of $g$‎. ‎we ‎will obtain lower and upper bounds for $d_2(g)$ in the case where ‎the sets $e_g(x)={yin g:[y,x,x]=1}$ are subgroups of $g$ for ‎all $xin g$‎. ‎also the given examples illustrate that all the ‎bounds are sharp.