about
Probabilistic brains: knowns and unknownsImmune-mediated competition in rodent malaria is most likely caused by induced changes in innate immune clearance of merozoitesModel selection on solid ground: Rigorous comparison of nine ways to evaluate Bayesian model evidenceDirichlet multinomial mixtures: generative models for microbial metagenomicsGenerative embedding for model-based classification of fMRI data.Calibration of Boltzmann distribution priors in Bayesian data analysis.Complex sequencing rules of birdsong can be explained by simple hidden Markov processes.Model averaging, optimal inference, and habit formation.Assessing rbf networks using DELVE.Shrinkage-based similarity metric for cluster analysis of microarray data.Dividing organelle tracks into Brownian and motor-driven intervals by variational maximization of the Bayesian evidence.Effect of genotype imputation on genome-enabled prediction of complex traits: an empirical study with mice data.Spatio-temporal modeling and analysis of fMRI data using NARX neural network.Bayesian maximum entropy (two-dimensional) lifetime distribution reconstruction from time-resolved spectroscopic data.Quantitative analysis of immune response and erythropoiesis during rodent malarial infection.Computational psychiatry as a bridge from neuroscience to clinical applications.Blind source separation of hemodynamics from magnetic resonance perfusion brain images using independent factor analysis.Radiocarbon constraints on the glacial ocean circulation and its impact on atmospheric CO2Cluster analysis of gene expression dynamicsReceptive field inference with localized priors.Model-based analysis of multishell diffusion MR data for tractography: how to get over fitting problems.Artificial Neural Network-Based System for PET Volume Segmentation.In silico prediction of estrogen receptor subtype binding affinity and selectivity using statistical methods and molecular docking with 2-arylnaphthalenes and 2-arylquinolines.Fast, automated implementation of temporally precise blind deconvolution of multiphasic excitatory postsynaptic currents.Flat minima.Bayesian model comparison and parameter inference in systems biology using nested samplingDynamics of the force of infection: insights from Echinococcus multilocularis infection in foxes.Efficient inference of parsimonious phenomenological models of cellular dynamics using S-systems and alternating regression.Incorporating prior knowledge improves detection of differences in bacterial growth rateNetwork Plasticity as Bayesian InferencePredicting Market Impact Costs Using Nonparametric Machine Learning ModelsDecoding of Covert Vowel Articulation Using Electroencephalography Cortical CurrentsDetecting differential growth of microbial populations with Gaussian process regression.Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM).Model-based analyses: Promises, pitfalls, and example applications to the study of cognitive control.Statistical physics theory of query learning by an ensemble of higher-order neural networks.Assessment of the water quality monitoring network of the Piabanha River experimental watersheds in Rio de Janeiro, Brazil, using autoassociative neural networks.Micromechanics of sea ice frictional slip from test basin scale experiments.Sparse Bayesian Learning for Obtaining Sparsity of EEG Frequency Bands Based Feature Vectors in Motor Imagery Classification.Prehepatic secretion and disposal of insulin in obese adolescents as estimated by three-hour, eight-sample oral glucose tolerance tests.
P2860
Q26852897-A85BD74A-5C1A-4DC6-836D-14CBEC0496AEQ28539048-EFD366AD-A606-40D9-B6EB-B3DB767D95B8Q28649935-7F621BCF-F833-4836-9D0F-82CE1F36975CQ28732242-C2FCCE6C-C5EA-46B1-8EAB-EB9DA84C1C89Q30000950-71509940-52D5-47D4-BD10-CF4CF7D5B37DQ30426607-96246978-F468-4C92-91F8-6AEE86AAFA2BQ30474152-FFEEE1A6-F6E3-4545-88A2-48F77478580DQ30581641-0A8D26CA-07C1-4033-B743-95754A6E9BC5Q30654270-5D32C5C9-036E-4FE2-B846-F01C0D3EBF61Q30816644-C27CF4CA-73DC-4018-AEA1-C32BAB7F1E5BQ30829986-8FC7AD37-4046-40FE-9DA5-D8E465D5607EQ30882056-8CA383CB-DB34-4C1F-A635-09B40A2D116EQ31040133-C0637CD5-1B21-4953-9769-512E94E30AB4Q31110357-3C935BEA-0E90-47A7-9924-F597EA334BB1Q33717041-1638D232-2F73-4584-9C95-9340941E1E4FQ33724477-C89DF2B1-762A-4E52-906F-4C67369CF145Q33809524-0D1A0B26-06BB-4F64-ABCF-123B673E2FB3Q33906381-72B199DF-E141-42BE-94E6-36E6DDBA5FC5Q34033786-5B1E8A92-6D23-4B99-84F9-60C41A2B4B1CQ34064141-0F368A02-E0BE-4390-9D62-7A70FB48712EQ34158998-86F2E1F3-EE7F-474A-ABA8-A70FB059EE0FQ34169928-EDD098FA-7A2B-4D96-826A-DF855C2E5B39Q34205634-2946900E-F637-4E49-A20B-E4CFDEB57F88Q34325675-A0F8FEFA-5863-42C4-B2F4-68D02723DD0DQ34422981-1B0B0C05-E9C4-434C-8868-A3DAA1E5BFCBQ35093600-5B758738-9991-44B9-A294-A3BB96B4009AQ35126544-E3EEE11C-92C5-406F-9DAF-ED2169240377Q35585965-CF0AA871-BD6E-45C8-A29F-036DE49B30F4Q35782453-C41AE458-8A29-41D2-AB71-9313B3C36E5DQ35834437-A72ECF39-2776-4FA9-8071-36E36AE65A5AQ35939524-6E0E652E-E386-45CD-A195-8B3931197230Q36859075-F8AC5E60-1F0F-4592-96D4-DBD305468460Q37619116-38D4B3AC-571F-44FD-986E-3241B73284B6Q37715779-5BAC5C88-932B-41B3-8301-6F941C4103B2Q37741095-2727E724-A908-4114-894E-AC8A3BCC1F04Q38547394-58A1A2F1-65F7-45F9-8F15-18D0AA00213FQ38635583-07FE9837-62B3-4045-9E47-31B84EB033A9Q39063356-718B1DE4-436C-4EB6-9729-CEF9F87771FAQ39632676-257C773A-617D-4AB7-BA16-A7DB10940556Q39796669-7C3A443B-AC79-465D-A851-C1F9B33084B6
P2860
description
wetenschappelijk artikel
@nl
наукова стаття, опублікована в травні 1992
@uk
name
Bayesian Interpolation
@en
Bayesian Interpolation
@nl
type
label
Bayesian Interpolation
@en
Bayesian Interpolation
@nl
prefLabel
Bayesian Interpolation
@en
Bayesian Interpolation
@nl
P1433
P1476
Bayesian Interpolation
@en
P2093
David J. C. MacKay
P304
P356
10.1162/NECO.1992.4.3.415
P577
1992-05-01T00:00:00Z