We define and study a new type of quantum oracle, the quantum conditional oracle, which provides oracle access to the conditional probabilities associatedwith an underlying distribution. Amongst other properties, we (a) obtain speed-ups over the best known quantum algorithms for identity testing, equivalence testing and uniformity testing of probability distributions; (b) study the power of the...

Upon varying parameters in a sensitivity analysis of a Bayesian network, the standard approach is to co-vary the parameters from the same conditional distribution such that their proportions remain the same. Alternative co-variation schemes are, however, possible. We theoretically investigate the effects of using alternative co-variation schemes on the so-called sensitivity function, and conclu...

The paper solves the problem of minimization of the Kullback divergence between a partially known and a completely known probability distribution. It considers two probability distributions of a random vector (u1, x1, . . . , uT , xT ) on a sample space of 2T dimensions. One of the distributions is known, the other is known only partially. Namely, only the conditional probability distributions ...

The objective of this study was to present a novel bivariate distribution, which we denoted as the odd generalized exponential gompertz(BOGE-G) distribution. Other well-known models included in one include gompertz, exponential, and gompertz model introduced here is Marshall-Olkin type [16]. marginals new distribution have proposed by[7]. Closed forms exist for both joint probability density fu...

We study semiparametric efficiency bounds and efficient estimation of parameters defined through general moment restrictions with missing data. Identification relies on auxiliary data containing information about the distribution of the missing variables conditional on proxy variables that are observed in both the primary and the auxiliary database, when such distribution is common to the two d...

In the paper a finite-capacity discrete-time queueing system with geometric interarrival times and generally distributed processing is studied. Every time when service station becomes idle it goes for vacation of random duration that can be treated as power-saving mechanism. Application single policy one way to achieve symmetry in terms operating costs. A differential equations transient condit...

We study semiparametric efficiency bounds and efficient estimation of parameters defined through general moment restrictions with missing data. Identification relies on auxiliary data containing information about the distribution of the missing variables conditional on proxy variables that are observed in both the primary and the auxiliary database, when such distribution is common to the two d...

Markov networks (MNs) are a powerful way to compactly represent a joint probability distribution, but most MN structure learning methods are very slow, due to the high cost of evaluating candidates structures. Dependency networks (DNs) represent a probability distribution as a set of conditional probability distributions. DNs are very fast to learn, but the conditional distributions may be inco...