Aoristic data can be described by a marked point process in time which the points cannot observed directly but are known to lie observable intervals, marks. We consider Bayesian state estimation for latent when marks modeled terms of an alternating renewal equilibrium and prior is Markov process. derive posterior distribution, estimate its parameters present some examples that illustrate influe...

the current study addresses an estimation of investor's optimal portfolio under conditions of uncertainty by using a combination of artificial neural network and markowitz models. for this purpose, such assets as stock prices, house prices, coin and bonds price are used with monthly data over the period 1378-1392. three variables including inflation uncertainty, oil uncertainty and free ma...

This paper considers the problem of computing Bayesian estimates both states and model parameters for nonlinear state-space models. Generally, this does not have a tractable solution approximations must be utilised. In work, variational approach is used to provide an assumed density which approximates desired, intractable, distribution. The deterministic results in optimisation standard form. D...