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1
Understanding predictive information criteria for Bayesian models
Understanding predictive information criteria for Bayesian models
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Understanding predictive information criteria for Bayesian models

Statistics and computing, 2014-11, Vol.24 (6), p.997-1016 [Peer Reviewed Journal]

Springer Science+Business Media New York 2013 ;ISSN: 0960-3174 ;EISSN: 1573-1375 ;DOI: 10.1007/s11222-013-9416-2

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2
Comparison of Bayesian predictive methods for model selection
Comparison of Bayesian predictive methods for model selection
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Comparison of Bayesian predictive methods for model selection

Statistics and computing, 2017-05, Vol.27 (3), p.711-735 [Peer Reviewed Journal]

The Author(s) 2016 ;ISSN: 0960-3174 ;EISSN: 1573-1375 ;DOI: 10.1007/s11222-016-9649-y

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3
Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation
Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation
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Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation

Statistics and computing, 2019-09, Vol.29 (5), p.891-913 [Peer Reviewed Journal]

The Author(s) 2018 ;ISSN: 0960-3174 ;EISSN: 1573-1375 ;DOI: 10.1007/s11222-018-9844-0

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4
What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?
What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?
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What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?

Computational statistics, 2021-09, Vol.36 (3), p.2009-2031 [Peer Reviewed Journal]

This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2020 ;This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2020. ;ISSN: 0943-4062 ;EISSN: 1613-9658 ;DOI: 10.1007/s00180-020-00999-9

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5
Statistics for High-Dimensional Data: Methods, Theory and Applications
Statistics for High-Dimensional Data: Methods, Theory and Applications
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Statistics for High-Dimensional Data: Methods, Theory and Applications

Springer-Verlag Berlin Heidelberg 2011 ;ISSN: 0172-7397 ;ISBN: 9783642201929 ;ISBN: 364220192X ;ISBN: 3642201911 ;ISBN: 9783642201912 ;EISBN: 9783642201929 ;EISBN: 364220192X ;DOI: 10.1007/978-3-642-20192-9 ;OCLC: 752337613

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6
Bayesian computation: a summary of the current state, and samples backwards and forwards
Bayesian computation: a summary of the current state, and samples backwards and forwards
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Bayesian computation: a summary of the current state, and samples backwards and forwards

Statistics and computing, 2015-07, Vol.25 (4), p.835-862 [Peer Reviewed Journal]

The Author(s) 2015 ;ISSN: 0960-3174 ;EISSN: 1573-1375 ;DOI: 10.1007/s11222-015-9574-5

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7
Approximate Bayesian computational methods
Approximate Bayesian computational methods
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Approximate Bayesian computational methods

Statistics and computing, 2012-11, Vol.22 (6), p.1167-1180 [Peer Reviewed Journal]

Springer Science+Business Media, LLC 2011 ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0960-3174 ;EISSN: 1573-1375 ;DOI: 10.1007/s11222-011-9288-2

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8
Estimation of prediction error by using K-fold cross-validation
Estimation of prediction error by using K-fold cross-validation
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Estimation of prediction error by using K-fold cross-validation

Statistics and computing, 2011-04, Vol.21 (2), p.137-146 [Peer Reviewed Journal]

Springer Science+Business Media, LLC 2009 ;ISSN: 0960-3174 ;EISSN: 1573-1375 ;DOI: 10.1007/s11222-009-9153-8

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9
Hilbert space methods for reduced-rank Gaussian process regression
Hilbert space methods for reduced-rank Gaussian process regression
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Hilbert space methods for reduced-rank Gaussian process regression

Statistics and computing, 2020-03, Vol.30 (2), p.419-446 [Peer Reviewed Journal]

The Author(s) 2019 ;ISSN: 0960-3174 ;EISSN: 1573-1375 ;DOI: 10.1007/s11222-019-09886-w

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10
Design of computer experiments: space filling and beyond
Design of computer experiments: space filling and beyond
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Design of computer experiments: space filling and beyond

Statistics and computing, 2012-05, Vol.22 (3), p.681-701 [Peer Reviewed Journal]

Springer Science+Business Media, LLC 2011 ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0960-3174 ;EISSN: 1573-1375 ;DOI: 10.1007/s11222-011-9242-3

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11
Sequential design of computer experiments for the estimation of a probability of failure
Sequential design of computer experiments for the estimation of a probability of failure
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Sequential design of computer experiments for the estimation of a probability of failure

Statistics and computing, 2012-05, Vol.22 (3), p.773-793 [Peer Reviewed Journal]

Springer Science+Business Media, LLC 2011 ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0960-3174 ;EISSN: 1573-1375 ;DOI: 10.1007/s11222-011-9241-4

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12
Mean and median bias reduction in generalized linear models
Mean and median bias reduction in generalized linear models
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Mean and median bias reduction in generalized linear models

Statistics and computing, 2020-02, Vol.30 (1), p.43-59 [Peer Reviewed Journal]

Springer Science+Business Media, LLC, part of Springer Nature 2019 ;ISSN: 0960-3174 ;EISSN: 1573-1375 ;DOI: 10.1007/s11222-019-09860-6

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13
Statistical analysis of differential equations: introducing probability measures on numerical solutions
Statistical analysis of differential equations: introducing probability measures on numerical solutions
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Statistical analysis of differential equations: introducing probability measures on numerical solutions

Statistics and computing, 2017, Vol.27 (4), p.1065-1082 [Peer Reviewed Journal]

The Author(s) 2016 ;ISSN: 0960-3174 ;EISSN: 1573-1375 ;DOI: 10.1007/s11222-016-9671-0

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14
A deep learning approach for Parkinson’s disease diagnosis from EEG signals
A deep learning approach for Parkinson’s disease diagnosis from EEG signals
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A deep learning approach for Parkinson’s disease diagnosis from EEG signals

Neural computing & applications, 2020-08, Vol.32 (15), p.10927-10933 [Peer Reviewed Journal]

The Natural Computing Applications Forum 2018 ;The Natural Computing Applications Forum 2018. ;ISSN: 0941-0643 ;EISSN: 1433-3058 ;DOI: 10.1007/s00521-018-3689-5

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15
Prairie Dog Optimization Algorithm
Prairie Dog Optimization Algorithm
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Prairie Dog Optimization Algorithm

Neural computing & applications, 2022-11, Vol.34 (22), p.20017-20065 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 ;The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. ;ISSN: 0941-0643 ;EISSN: 1433-3058 ;DOI: 10.1007/s00521-022-07530-9

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16
On optimal multiple changepoint algorithms for large data
On optimal multiple changepoint algorithms for large data
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On optimal multiple changepoint algorithms for large data

Statistics and computing, 2017-03, Vol.27 (2), p.519-533 [Peer Reviewed Journal]

The Author(s) 2016 ;Attribution - ShareAlike ;ISSN: 0960-3174 ;EISSN: 1573-1375 ;DOI: 10.1007/s11222-016-9636-3

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17
Proximal Markov chain Monte Carlo algorithms
Proximal Markov chain Monte Carlo algorithms
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Proximal Markov chain Monte Carlo algorithms

Statistics and computing, 2016-07, Vol.26 (4), p.745-760 [Peer Reviewed Journal]

The Author(s) 2015 ;ISSN: 0960-3174 ;EISSN: 1573-1375 ;DOI: 10.1007/s11222-015-9567-4

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18
An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets
An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets
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An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets

Neural computing & applications, 2019-11, Vol.31 (11), p.7041-7053 [Peer Reviewed Journal]

The Natural Computing Applications Forum 2018 ;Neural Computing and Applications is a copyright of Springer, (2018). All Rights Reserved. ;ISSN: 0941-0643 ;EISSN: 1433-3058 ;DOI: 10.1007/s00521-018-3521-2

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19
An adaptive sequential Monte Carlo method for approximate Bayesian computation
An adaptive sequential Monte Carlo method for approximate Bayesian computation
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An adaptive sequential Monte Carlo method for approximate Bayesian computation

Statistics and computing, 2012-09, Vol.22 (5), p.1009-1020 [Peer Reviewed Journal]

Springer Science+Business Media, LLC 2011 ;ISSN: 0960-3174 ;EISSN: 1573-1375 ;DOI: 10.1007/s11222-011-9271-y

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20
Goodness-of-fit indices for partial least squares path modeling
Goodness-of-fit indices for partial least squares path modeling
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Goodness-of-fit indices for partial least squares path modeling

Computational statistics, 2013-04, Vol.28 (2), p.565-580 [Peer Reviewed Journal]

The Author(s) 2012 ;Springer-Verlag Berlin Heidelberg 2013 ;ISSN: 0943-4062 ;EISSN: 1613-9658 ;DOI: 10.1007/s00180-012-0317-1

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