ライフサイエンスコーパス: maximum a posteriori

1 maximization (3DOSEM) and the 3-dimensional maximum a posteriori (3DMAP).

2 Unlike many other maximum a posteriori approaches, our method offers highl

3 The application of a maximum-a-posteriori Bayesian inference method identifie

4 stimulus integration, we used nonparametric maximum a posteriori decoding to compare the ability of

5 takes-all (WTA) models and a Bayesian model, maximum a posteriori estimate (MAP), to determine which

6 graph formulation can be used for obtaining maximum a posteriori estimates from models or optimizati

7 We calculated maximum a posteriori estimates of song spectrograms usin

8 in a corresponding one-dimensional family of maximum a posteriori estimates that interpolate smoothly

9 fice uncertainty quantification by computing maximum a posteriori estimates, or quantify the uncertai

10 clique potential functions in the MRF so its maximum a posteriori estimation can be reduced to the we

11 a sequence of transforms of an atlas, and a maximum a posteriori estimation framework.

12 izing it for global sensitivity analysis and maximum a posteriori estimation in a synthetic metabolic

13 Further, as an extension, maximum a posteriori estimation is provided.

14 matrix can be used as a structured prior for maximum a posteriori estimation of neural activity patte

15 entation to segment the minimum distance and maximum a posteriori estimation to infer de novo CNVs fr

16 Coupled with segmentation and maximum a posteriori estimation, our algorithm compares

17 ithm aims at robustness by using a priorless maximum a posteriori estimator and at efficiency by a dy

18 We propose a maximum a posteriori estimator based on composite likeli

19 s used to analyze the parametric behavior of maximum a posteriori inference calculations for graphica

20 an algorithm that provably reaches the MAP (maximum a posteriori) inference solution, but does so us

21 te graphical models that learn via an online maximum a posteriori learning algorithm could provide su

22 round model alleviates the drawbacks of MAP (maximum a posteriori log likelihood) scores.

23 n correction and a two-dimensional iterative maximum a posteriori (MAP) algorithm using attenuation c

24 e estimation process of parameters through a maximum a posteriori (MAP) Bayesian method to facilitate

25 We obtained very high Maximum a Posteriori (MAP) classification with a mixture

26 Maximum a posteriori (MAP) common secondary structures,

27 n between the two modalities compared to the maximum a posteriori (MAP) estimator.

28 A moving detector with Maximum a Posteriori (MAP) further achieved radiation po

29 tions on the time-frequency plane that yield maximum a posteriori (MAP) spectral estimates that are c

30 e reconstructed with the fully 3-dimensional maximum a posteriori method, and CT images were reconstr

31 e done in linear time using forward-backward maximum-a-posteriori methods.

32 Second, we demonstrate that the Pointwise Maximum a posteriori (PMAP) HMM decoding procedure yield

33 The method follows a maximum a posteriori principle to form a novel network s

34 iterative reconstruction algorithm utilizing maximum-a-posteriori principles and integrating the stat

35 algorithm (PRSSA) was developed based on the maximum a posteriori probability (MAP) estimate.

36 ng randomized data to determine the critical maximum a posteriori probability (MAP) values for statis

37 s expectation maximization and 3-dimensional maximum a posteriori probability (MAP3D) algorithms.

38 dexamethasone plasma concentrations by using maximum a posteriori probability estimation; we evaluate

39 The software is also capable of maximum a posteriori probability image estimation (MAP-S

40 e, i.e., that best "explains" it, called the maximum a posteriori skeleton.