Sentence examples for stochastic product from inspiring English sources

Gross mostly agreed with me, adding that a big challenge is helping customers who purchase services from machine intelligence startups understand what it means to rely on a stochastic product.

These polarimetric models are derived as stochastic product models [7, 8] of a non-Gaussian texture term and a multivariate Gaussian-based speckle term, and can be described by the class of models known as Scale Mixture of Gaussian (SMoG) models.

Tavakkoli-Moghaddam et al. (2007) suggested a novel formulation using the QAP to concurrent design of the optimal machine and cell layouts in a single time period planning horizon of a cellular manufacturing system (CMS) by considering the stochastic independent product demands with known normal PDF.

This structural characterization leads to the definition of SΠ2-nets (Stochastic Parametric Product-form Petri nets).

We apply the proposed approach to an exemplary manufacturing process with stochastic demand and product yield and compare its performance with an 'ideal' solution derived from a mixed integer programming formulation.

Even if the efficient BLAS-based fortran subroutines for linear algebra are used for the classical stochastic matrix/probability vector product (eq. 1), the sparsity of the Bacteria-net network is not adequately exploited.

This paper models the stochastic diffusion of a product in the market as a geometric Brownian motion (GBM) process that has a time-varying drift rate.

Noticing that the sample orbit of fractional Brownian motion is not differentiable almost everywhere in the classical sense, we consider the stochastic convolution in the product space H: z ( t ) = ∫ 0 t S ( t − s ) Φ d B H ( t ) : = ( ∫ 0 t S 1 ( t − s ) d B H ( t ) ∫ 0 t S 2 ( t − s ) Φ 2 d B H ( t ) ) ≜ ( z 1 ( t ) z 2 ( t ) ). (3.9).

Each virtual assay was repeated three times to represent inter-experimental replicates and ensure that results were not a product of stochastic effects inherent in the model.

MCM extends Harcombe et al.'s approach to more versatile microbial ecological models that include, for example, arbitrary dynamical environmental variables (e.g. pH and temperature), stochastic processes, phage predation, product inhibition, arbitrary uptake kinetics, reaction energetics and gene regulation.

As mentioned in our responses to Reviewer 1, in contrast to COMETS, MCM can accommodate more versatile microbial ecological models that include, for example, dynamical environmental variables (such as pH or temperature, that can change in response to microbial metabolism), stochastic processes, phage predation, product inhibition, arbitrary uptake kinetics, reaction energetics and gene regulation.