Fig. 12 Approximate framework for S λ,t averaging over y ′(t), e ′(t) and y π,t. a Approximate Framework 1. b Approximate Framework 2 2.
Fig. 12 Approximate framework for S λ,t averaging over y ′(t), e ′(t) and y π,t. a Approximate Framework 1. b Approximate Framework 2. Compute the extrinsic information from the channel decoder, i.e., the PDF of L E (L A (y π,t,S π,t ),y ′(t),e ′(t)).
Ph.D. thesis, Cornell University] and it provides a general, though approximate, framework that is amenable to systematic improvements and is flexible enough to incorporate the dynamical effects of a changing shape, different rheologies and complex rotational histories.
The framework equivalent to (18) is shown in Fig. 12 (a), which is denoted as approximate framework 1, and the computation of S λ,t (S π,t ) in (18) can be divided into the following three steps: 1. Compute the PDF of L A (y π,t,S π,t ) and (phantom {dot {i}!}f_{mathbf {y}_{pi,t}}).
To achieve above, the approximate framework 2 shown in Fig. 12 (b) is considered, but different from that we set S π,t and S λ,t as (text {MMSE}_{text {ap}}^{Y}mathbf {I}_{K}) and (text {MMSE}_{text {ext}}^{Y}mathbf {I}_{K}).
However, a molecular-clock-based time estimate does surely provide an approximate framework for phylogeographic inferences.
In this paper, we propose an approximate model driven framework for efficient soft error analysis in processors.
We employ an approximation framework using Monte Carlo methods and obtain an optimization procedure based on particle representations and approximate computations.
Therefore, we use an approximate inference framework to compute the estimates of the marginal pdfs of the variables, called beliefs.
Meanwhile, the proposed weighted approach is combined with a decoupling approach and a sequential approximate optimization framework.
The statistical estimates of the model parameters are compared with the analytical assessment made possible by an approximate analytical framework.
