The notion of statistical computations with respect to an uncertain locally adaptive distance measure is uncharted territory, Which need new algorithms for

numerical integration and for solving differential equations.

Schiesser examines the

numerical integration, or solution, of a system of partial differential equations that describe a combination of chemical reaction and diffusion, that is, reaction-diffusion PDEs, which correspond to a system that British mathematician Alan Turing (1912-54) discussed, and is therefore known as a Turing model.

In some cases the

numerical integration of the equations of the stress-deformed state with the movable due to continuous corrosion boundary was discussed in [18] for example.

Several researchers have applied

numerical integration methods for solving the classical swing equations [1], [2], [3] to obtain the behavior of the power angles and the rotational speeds in the time domain.

The rest of the paper compares two methods for applying Bayesian inference in estimation of the value of information; the first uses

numerical integration and the second the Metropolis-Hastings algorithm.

A well-known early technique is the selective reduced integration technique [2,3], in which the order of

numerical integration for calculating the shear related part of the stiffness matrix is deliberately reduced.

Firstly, for the solution of systems of differential equations to use implicit

numerical integration methods.

However,

numerical integration of nonpolynomials over polyhedral elements is still immature, since the polyhedral elements need to be partitioned into numerous numbers of subelements in which the

numerical integration will be carried out or a lot of quadrature points and weights are needed to achieve good accuracy in integration over the polyhedral elements.

Mathematicians have developed a wide range of

numerical integration techniques for solving the ordinary differential equations (ODEs) that correspond to the continuous state of dynamic systems.

These simulations compare the estimated received EM field at an observation point above flat and lossy ground (particularly only the scattered field is calculated since the Line of Sight field is easily and analytically derivable), under two approaches: (a) SPM method [9]-[11] and (b)

Numerical integration of the corresponding integral representations.

The instantaneous angular displacement of the IMU ([theta]) about the axis of rotation (O) was estimated through

numerical integration of [?

for several values of h, [increment of x], and [DELTA]t and the experimental spatial, temporal, and

numerical integration convergence orders that we denote by [p.