Earth science satellite missions currently require orbit determination solutions with position accuracies to within a centimeter. The estimation of empirical accelerations has become commonplace in precise orbit determination (POD) for Earth-orbiting satellites. Dynamic model compensation (DMC) utilizes an exponentially time-correlated system noise process, known as a first-order GaussMarkov pr...

This article studies the E-Bayesian estimation of unknown parameter Lomax distribution based on generalized Type-I hybrid censoring. Under square error loss and LINEX functions, we get compare its effectiveness with Bayesian estimation. To measure estimation, expectation mean (E-MSE) is introduced. With Markov chain Monte Carlo technology, estimations are computed. Metropolis–Hastings algorithm...

We propose a new method for designing quantum search algorithms for finding a “marked” element in the state space of a classical Markov chain. The algorithm is based on a quantumwalk à la Szegedy [Sze04] that is defined in terms of the Markov chain. The main new idea is to apply quantum phase estimation to the quantum walk in order to implement an approximate reflection operator, which is used ...

We propose a new method for designing quantum search algorithms for finding a “marked” element in the state space of a classical Markov chain. The algorithm is based on a quantumwalk à la Szegedy [Sze04] that is defined in terms of the Markov chain. The main new idea is to apply quantum phase estimation to the quantum walk in order to implement an approximate reflection operator, which is used ...

Abstmet-We consider first the estimation of the order, i.e., the number of states, of a discrete-time finite-alphabet stationary ergodic hidden Markov source (HMS). Our estimator uses a description of the observed data in terms of a uniquely deadable code with respect to a mixture distriiw obtained by suitably mixing a parametric family of dletribntiom on the observation space. This procedure a...

One of the most important challenges in understanding expert perception is in determining what information in a complex scene is most valuable (reliable) for a particular task, and how experts learn to exploit it. For the task of parameter estimation given multiple independent sources of data, Bayesian data fusion provides a solution to this problem that involves promoting data to a common para...

This paper investigates Bayesian estimation for Gaussian Markov random elds. In particular, a new class of inhomogeneous model is proposed. This inhomogeneous model uses a Markov random eld to describe spatial variation of the smoothing parameter in a second random eld which describes the spatial variation in the observed intensity image. The coupled Markov random elds will be used as prior dis...

We analyze several random random walks on one-dimensional lattices using spectral analysis and probabilistic methods. Through our analysis, we develop insight into the pre-asymptotic convergence of Markov chains.