Image-quality evaluation and model selection with maximum a posteriori probability
暂无分享,去创建一个
Arnold J. den Dekker | Jarmo Fatermans | Annick De Backer | Sandra Van Aert | J. Fatermans | A. Dekker | S. Aert | A. Backer
[1] D Van Dyck,et al. MULTEM: A new multislice program to perform accurate and fast electron diffraction and imaging simulations using Graphics Processing Units with CUDA. , 2015, Ultramicroscopy.
[2] Piet M. T. Broersen,et al. On Finite Sample Theory for Autoregressive Model Order Selection , 1993, IEEE Trans. Signal Process..
[3] I. J. Myung,et al. Applying Occam’s razor in modeling cognition: A Bayesian approach , 1997 .
[4] S Van Aert,et al. The maximum a posteriori probability rule for atom column detection from HAADF STEM images. , 2019, Ultramicroscopy.
[5] J. Ridder,et al. DIAMONDS: A new Bayesian nested sampling tool - Application to peak bagging of solar-like oscillations , 2014, 1408.2515.
[6] Piet M. T. Broersen,et al. On the penalty factor for autoregressive order selection in finite samples , 1996, IEEE Trans. Signal Process..
[7] D. Van Dyck,et al. Maximum likelihood estimation of structure parameters from high resolution electron microscopy images. Part I: a theoretical framework. , 2005 .
[8] J. Sijbers,et al. Estimation of unknown structure parameters from high-resolution (S)TEM images: what are the limits? , 2013, Ultramicroscopy.
[9] I. J. Myung,et al. The Importance of Complexity in Model Selection. , 2000, Journal of mathematical psychology.
[10] A. J. den Dekker,et al. Maximum likelihood estimation of structure parameters from high resolution electron microscopy images. Part II: a practical example. , 2005, Ultramicroscopy.
[11] J Sijbers,et al. StatSTEM: An efficient approach for accurate and precise model-based quantification of atomic resolution electron microscopy images. , 2016, Ultramicroscopy.
[12] Jian Li,et al. Multi-model approach to model selection , 2004, Digit. Signal Process..
[13] B. G. Quinn,et al. The determination of the order of an autoregression , 1979 .
[14] S Bals,et al. Quantitative atomic resolution mapping using high-angle annular dark field scanning transmission electron microscopy. , 2009, Ultramicroscopy.
[15] O. Landon-Cardinal,et al. Random vs realistic amorphous carbon models for high resolution microscopy and electron diffraction , 2013 .
[16] J. Verbeeck,et al. Progress and new advances in simulating electron microscopy datasets using MULTEM. , 2016, Ultramicroscopy.
[17] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[18] Yves Rosseel,et al. On the Definition of Signal-To-Noise Ratio and Contrast-To-Noise Ratio for fMRI Data , 2013, PloS one.
[19] S. Bals,et al. Procedure to count atoms with trustworthy single-atom sensitivity , 2013 .
[20] J. Fatermans,et al. Atom column detection from simultaneously acquired ABF and ADF STEM images. , 2020, Ultramicroscopy.
[21] P. Goos,et al. Model-based electron microscopy : from images toward precise numbers for unknown structure parameters , 2012 .
[22] Y. Selen,et al. Model-order selection: a review of information criterion rules , 2004, IEEE Signal Processing Magazine.
[23] P. Nellist,et al. Single Atom Detection from Low Contrast-to-Noise Ratio Electron Microscopy Images. , 2018, Physical review letters.
[24] E. B. Wilson. Probable Inference, the Law of Succession, and Statistical Inference , 1927 .
[25] P D Nellist,et al. Probe integrated scattering cross sections in the analysis of atomic resolution HAADF STEM images. , 2013, Ultramicroscopy.
[26] H. Akaike. A new look at the statistical model identification , 1974 .