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Fitting models to data: Accuracy, Speed, Robustness

Andrew Fitzgibbon
2015 Procedings of the British Machine Vision Conference 2015  
options for curves and surfaces; how to deal with missing data; how to deal with outliers, including how to optimize functions with robust kernels.  ...  In vision and machine learning, almost everything we do may be considered to be a form of model fitting.  ...  options for curves and surfaces; how to deal with missing data; how to deal with outliers, including how to optimize functions with robust kernels.  ... 
doi:10.5244/c.29.1 dblp:conf/bmvc/Fitzgibbon15 fatcat:7nhummb52zcojey2m4efqmiwwu

Fitting Statistical Distributions to Data in Hurricane Modeling [chapter]

2010 Handbook of Fitting Statistical Distributions with R  
Fitting probability distributions to hurricane related data is an essential activity in hurricane planning, designing structures, and catastrophe modeling applications.  ...  The primary objective in this paper is to describe the background and applications of historical hurricane data fitting, the operational aspects of which have dictated adjustments to the standard methodology  ...  Hence, fitting methodologies must be robust to such perturbations to the data bases. This environment has a major impact on the focus of our statistical concerns.  ... 
doi:10.1201/b10159-38 fatcat:fo5kmkimgnep3fy7th37umo42q

Fitting Statistical Distributions to Data in Hurricane Modeling

Mark E. Johnson, Charles C. Watson
2007 American Journal of Mathematical and Management Sciences  
Fitting probability distributions to hurricane related data is an essential activity in hurricane planning, designing structures, and catastrophe modeling applications.  ...  The primary objective in this paper is to describe the background and applications of historical hurricane data fitting, the operational aspects of which have dictated adjustments to the standard methodology  ...  Hence, fitting methodologies must be robust to such perturbations to the data bases. This environment has a major impact on the focus of our statistical concerns.  ... 
doi:10.1080/01966324.2007.10737710 fatcat:q42rxqz7nbhrlbuxvdrgixqzui

Fitting the ratcliff diffusion model to experimental data

Joachim Vandekerckhove, Francis Tuerlinckx
2007 Psychonomic Bulletin & Review  
J.V. and F.T. thank Jeff Rouder for providing the data used in the second application example, and Eric-Jan Wagenmakers and Andrew Heathcote for helpful comments on earlier drafts of this article.  ...  Correspondence concerning this article should be addressed to J. Vandekerckhove, Department of Psychology, Tiensestraat 102, B-3000 Leuven, Belgium (e-mail: joachim.vandekerckhove@ psy.kuleuven.be).  ...  analysis; and second, a mixture model is fitted to the data.  ... 
doi:10.3758/bf03193087 pmid:18229471 fatcat:6ccjil64ljcy5g2k2xjevb5ana

Fitting drift-diffusion decision models to trial-by-trial data [article]

Quentin Feltgen, Jean Daunizeau
2020 biorxiv/medrxiv   pre-print
Fitting a DDM to empirical data then allows one to interpret observed group or condition differences in terms of a change in the underlying model parameters.  ...  Using numerical simulations, we show that this approach enables one to extract relevant information from trial-by-trial variations of RT data that would typically be buried in the empirical distribution  ...  AUTHOR CONTRIBUTIONS All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.  ... 
doi:10.1101/2020.01.30.925123 fatcat:3ozh65a55fdfhgfowj32fnjffq

Modeling in Forestry Using Mixture Models Fitted to Grouped and Ungrouped Data

Eric K. Zenner, Mahdi Teimouri
2021 Forests  
Since forestry data frequently occur both in grouped (classified) and ungrouped (raw) forms, the EM algorithm was applied to explore the goodness-of-fit of the gamma, log-normal, and Weibull mixture distributions  ...  The EM-based goodness-of-fit was further compared against a nonparametric kernel-based density estimation (NK) model and the recently popularized gamma-shaped mixture (GSM) models using the ungrouped data  ...  data, (d) 3-component mixture models fitted to ungrouped data, (e) 4-component mixture models fitted to grouped data, and (f) 4-component mixture models fitted to ungrouped data.  ... 
doi:10.3390/f12091196 fatcat:7hmisrlygzbi7agzkhwyzoddiq

To fit or not to fit: Model-based Face Reconstruction and Occlusion Segmentation from Weak Supervision [article]

Chunlu Li, Andreas Morel-Forster, Thomas Vetter, Bernhard Egger, Adam Kortylewski
2021 arXiv   pre-print
Supervised occlusion segmentation is a viable solution to avoid the fitting of occluded face regions, but it requires a large amount of annotated training data.  ...  fitting is not negatively affected and reaches higher overall reconstruction accuracy on pixels showing the face.  ...  The authors would like to express their sincere gratitude to the sponsors, as well as Tatsuro Koizumi and William A. P. Smith who offered the MoFA re-implementation.  ... 
arXiv:2106.09614v1 fatcat:td2jdoeorffi3mqgwsuz4f3eqa

An Overcomplete Approach to Fitting Drift-Diffusion Decision Models to Trial-By-Trial Data

Q. Feltgen, J. Daunizeau
2021 Frontiers in Artificial Intelligence  
Fitting a DDM to empirical data then allows one to interpret observed group or condition differences in terms of a change in the underlying model parameters.  ...  In this note, we propose a fast and efficient approach to parameter estimation that relies on fitting a "self-consistency" equation that RT fulfill under the DDM.  ...  In turn, alternative statistical approaches to parameter estimation have been proposed, which can exploit predictable inter-trial variations of DDM variables to fit the model to RT data (Wabersich and  ... 
doi:10.3389/frai.2021.531316 pmid:33898982 pmcid:PMC8064018 fatcat:rmipghy2gjgdxbluhc67z6mmty

Measuring Fit of Sequence Data to Phylogenetic Model: Gain of Power using Marginal Tests [article]

Peter J. Waddell, Rissa Ota, David Penny
2008 arXiv   pre-print
Testing fit of data to model is fundamentally important to any science, but publications in the field of phylogenetics rarely do this.  ...  It is seen that the most general test does not reject the fit of data to model (p~0.5), but the marginalized tests do.  ...  Acknowledgements This work was supported by NIH grant 5R01LM008626 to PJW. Thanks to Mike Steel for helpful discussions.  ... 
arXiv:0812.5005v1 fatcat:eqzzvxzc7ba3pldl754e3bqmee

Fitting a biomechanical model of the folds to high-speed video data through bayesian estimation

Carlo Drioli, Gian Luca Foresti
2020 Informatics in Medicine Unlocked  
The procedure relies on a Bayesian non-stationary estimation of the biomechanical model parameters and state, to fit the folds edge position extracted from the high-speed video endoscopic data.  ...  To demonstrate the suitability of the procedure, the method is assessed on a set of audiovisual recordings featuring high-speed video endoscopic data from healthy subjects producing sustained voiced phonation  ...  Acknowledgments We wish to thank E. Bianco and G. Degottex for kindly providing the high-speed video recordings used in this paper.  ... 
doi:10.1016/j.imu.2020.100373 fatcat:ufr6dh25mjbwna3gi5skxv46qa

Robust fitting of 3D CAD models to video streams [chapter]

Christophe Meilhac, Chahab Nastar
1997 Lecture Notes in Computer Science  
This last step is performed by polygon fitting in an edge image. To account for false matches, we use a robust M-estimation both for camera parameter estimation and 2D feature extraction.  ...  We present a robust and accurate semi-automatic algorithm for registering and tracking a 3D geometric model in a 2D video stream.  ...  In this framework, we show interest in fitting a geometrical CAD-model of an object to a 2D image of the object in a complex sc~'ne (figure 4 and 5) .  ... 
doi:10.1007/3-540-63507-6_258 fatcat:n76nywgiezbppat3aobgx2y5hi

Fitting parameterized three-dimensional models to images

D.G. Lowe
1991 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Model-based recognition and motion tracking depends upon the ability to solve for projection and model parameters that will best fit a 3-D model to matching 2-D image features.  ...  These techniques allow model-based vision to be used for a much wider class of problems than was possible with previous methods.  ...  robust against missing and noisy data.  ... 
doi:10.1109/34.134043 fatcat:ejfgcnyjynbpxi26ftrug5ezze

On the Theoretical Guarantees for Parameter Estimation of Gaussian Random Field Models: A Sparse Precision Matrix Approach [article]

Sam Davanloo Tajbakhsh, Necdet Serhat Aybat, Enrique Del Castillo
2020 arXiv   pre-print
) matrix to the observed data.  ...  Iterative methods for fitting a Gaussian Random Field (GRF) model via maximum likelihood (ML) estimation requires solving a nonconvex optimization problem.  ...  The training data is used to fit GRF models using MLE-1, MLE-10, MLE-100 (see the paragraph above for their definitions), and SPS.  ... 
arXiv:1405.5576v5 fatcat:tvuedhkxsbcxlbcvcs2tc2bj7e

Mechanistic Models Fit to Variable Temperature Calorimetric Data Provide Insights into Cooperativity

Elihu C. Ihms, Ian R. Kleckner, Paul Gollnick, Mark P. Foster
2017 Biophysical Journal  
This work illustrates the potential of mechanistically constrained global fitting of binding data to yield the microscopic thermodynamic parameters essential for deciphering mechanisms of cooperativity  ...  We globally fit temperature-dependent isothermal titration calorimetry data for binding of 11 tryptophan ligands to the homo-undecameric trp RNA-binding Attenuation Protein from Bacillus stearothermophilus  ...  FIGURE 4 4 Nonadditive nearest-neighbor model provides a superior global fit to experimental data.  ... 
doi:10.1016/j.bpj.2017.02.031 pmid:28402876 pmcid:PMC5390055 fatcat:eqkt3yrb7vhmfavh4t7yd6sahy

Purposive Sample Consensus: A Paradigm for Model Fitting with Application to Visual Odometry [chapter]

Jianguo Wang, Xiang Luo
2015 Springer Tracts in Advanced Robotics  
It is applied to line fitting to explain its principles, and then to feature based visual odometry, which requires efficient, robust and precise model fitting.  ...  RANSAC (random sample consensus) is a robust algorithm for model fitting and outliers' removal, however, it is neither efficient nor reliable enough to meet the requirement of many applications where time  ...  speed and certainty of model fitting.  ... 
doi:10.1007/978-3-319-07488-7_23 fatcat:xrw7ajy27bafvdgajbuju6irhi
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