Loading
Error: Cannot Load Popup Box
Skip to hit list
Adjust your hit list
Further result pages
Mobile

A
A
A

A

English
Deutsch
Français
Español
Polski
Ελληνικά
Українська
中文
 Logged in as

Log Out

Login
BASIC
SEARCH
ADVANCED
SEARCH
HELP
BROWSING
SEARCH
HISTORY
Your search
Search For:
Entire Document
Title
Author
Subject
Boost open access documents
Find
Linguistics tools
Verbatim search
Additional word forms
Multilingual synonyms
Statistics
79 hits
in 72,247,077 documents
in 0.32 seconds
Please leave the following field blank:
Home
»
Search: F. P. A. Coolen
Hit List
Hit list
1.
Parametric probability distributions in reliability ∗
Open Access
Title:
Parametric probability distributions in reliability ∗
Author:
F. P. A. Coolen
F. P. A. Coolen
Minimize authors
Description:
In this paper, we present an overview of basic parametric probability distributions which are frequently used in reliability. We present some main characteristics of these distributions, and briefly discuss underlying assumptions related to their suitability as models for specific reliability scenarios.
In this paper, we present an overview of basic parametric probability distributions which are frequently used in reliability. We present some main characteristics of these distributions, and briefly discuss underlying assumptions related to their suitability as models for specific reliability scenarios.
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090806
Source:
http://maths.dur.ac.uk/stats/people/fc/pmdist.pdf
http://maths.dur.ac.uk/stats/people/fc/pmdist.pdf
Minimize
Document Type:
text
Language:
en
Subjects:
Binomial distribution ; Exponential distribution ; Gamma distribution ; Normal distribution ; Poisson distribution ; Weibull distribution
Binomial distribution ; Exponential distribution ; Gamma distribution ; Normal distribution ; Poisson distribution ; Weibull distribution
Minimize
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.8571
http://maths.dur.ac.uk/stats/people/fc/pmdist.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.8571
http://maths.dur.ac.uk/stats/people/fc/pmdist.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
2.
On Nonparametric Predictive Inference and Objective Bayesianism
Open Access
Title:
On Nonparametric Predictive Inference and Objective Bayesianism
Author:
F. P. A. Coolen
F. P. A. Coolen
Minimize authors
Description:
This paper consists of three main parts. First, we give an introduction to Hill’s assumption A(n) and to theory of interval probability, and an overview of recently developed theory and methods for nonparametric predictive inference (NPI), which is based on A(n) and uses interval probability to quantify uncertainty. Thereafter, we illustrate NPI...
This paper consists of three main parts. First, we give an introduction to Hill’s assumption A(n) and to theory of interval probability, and an overview of recently developed theory and methods for nonparametric predictive inference (NPI), which is based on A(n) and uses interval probability to quantify uncertainty. Thereafter, we illustrate NPI by introducing a variation to the assumption A(n), suitable for inference based on circular data, with applications to several data sets from the literature. This includes attention to comparison of two groups of circular data, and to grouped data. We briefly discuss such inference for multiple future observations. We end the paper with a discussion of NPI and objective Bayesianism.
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20140627
Source:
http://maths.dur.ac.uk/stats/people/fc/npi/
coolen
jlli.pdf
http://maths.dur.ac.uk/stats/people/fc/npi/
coolen
jlli.pdf
Minimize
Document Type:
text
Language:
en
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.104.7279
http://maths.dur.ac.uk/stats/people/fc/npi/coolenjlli.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.104.7279
http://maths.dur.ac.uk/stats/people/fc/npi/coolenjlli.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
3.
On nonparametric predictive inference and objective Bayesianism
Open Access
Title:
On nonparametric predictive inference and objective Bayesianism
Author:
F. P. A. Coolen
F. P. A. Coolen
Minimize authors
Description:
This paper consists of three main parts. First, we give an introduction to Hill’s assumption A (n) and to theory of interval probability, and an overview of recently developed theory and methods for nonparametric predictive inference (NPI), which is based on A (n) and uses interval probability to quantify uncertainty. Thereafter, we illustrate N...
This paper consists of three main parts. First, we give an introduction to Hill’s assumption A (n) and to theory of interval probability, and an overview of recently developed theory and methods for nonparametric predictive inference (NPI), which is based on A (n) and uses interval probability to quantify uncertainty. Thereafter, we illustrate NPI by introducing a variation to the assumption A (n), suitable for inference based on circular data, with applications to several data sets from the literature. This includes attention to comparison of two groups of circular data, and to grouped data. We briefly discuss such inference for multiple future observations. We end the paper with a discussion of NPI and objective Bayesianism.
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20080717
Source:
http://www.kent.ac.uk/secl/philosophy/jw/2005/progic/papers/
Coolen
.pdf
http://www.kent.ac.uk/secl/philosophy/jw/2005/progic/papers/
Coolen
.pdf
Minimize
Document Type:
text
Language:
en
Subjects:
A (n ; circular data ; exchangeability ; grouped data ; imprecise probabilities
A (n ; circular data ; exchangeability ; grouped data ; imprecise probabilities
Minimize
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.98.4252
http://www.kent.ac.uk/secl/philosophy/jw/2005/progic/papers/Coolen.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.98.4252
http://www.kent.ac.uk/secl/philosophy/jw/2005/progic/papers/Coolen.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
4.
Jury size and composition  a predictive approach
Open Access
Title:
Jury size and composition  a predictive approach
Author:
F. P. A. Coolen
F. P. A. Coolen
Minimize authors
Description:
We consider two basic aspects of juries that must decide on guilt verdicts, namely the size of juries and their composition in situations where society consists of subpopulations. We refer to the actual jury that needs to provide a verdict as the ‘first jury’, and as their judgement should reflect that of society, we consider an imaginary ‘seco...
We consider two basic aspects of juries that must decide on guilt verdicts, namely the size of juries and their composition in situations where society consists of subpopulations. We refer to the actual jury that needs to provide a verdict as the ‘first jury’, and as their judgement should reflect that of society, we consider an imaginary ‘second jury’ to represent society. The focus is mostly on a lower probability of a guilty verdict by the second jury, conditional on a guilty verdict by the first jury, under suitable exchangeability assumptions between this second jury and the first jury. Using a lower probability of a guilty verdict naturally provides a ‘benefit of doubt to the defendant’ robustness of the inference. By use of a predictive approach, no assumptions on the guilt of a defendant are required, which distinguishes this approach from those presented before. The statistical inferences used in this paper are relatively straightforward, as only cases are considered where the lower probabilities according to
Coolen
’s Nonparametric Predictive Inference for Bernoulli random quantities [5] and Walley’s
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20101013
Source:
http://www.dur.ac.uk/brett.houlding/resources/isipta07juries.pdf
http://www.dur.ac.uk/brett.houlding/resources/isipta07juries.pdf
Minimize
Document Type:
text
Language:
en
Subjects:
lower probability ; Nonparametric Predictive Inference ; representation of subpopulations
lower probability ; Nonparametric Predictive Inference ; representation of subpopulations
Minimize
DDC:
160 Logic
(computed)
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.130.1017
http://www.dur.ac.uk/brett.houlding/resources/isipta07juries.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.130.1017
http://www.dur.ac.uk/brett.houlding/resources/isipta07juries.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
5.
Learning from multinomial data: a nonparametric predictive alternative to the Imprecise Dirichlet Model
Open Access
Title:
Learning from multinomial data: a nonparametric predictive alternative to the Imprecise Dirichlet Model
Author:
F. P. A. Coolen
F. P. A. Coolen
Minimize authors
Description:
A new model for learning from multinomial data has recently been developed, giving predictive inferences in the form of lower and upper probabilities for a future observation. Apart from the past observations, no information on the sample space is assumed, so explicitly no assumptions are made on the number of possible categories. In this paper,...
A new model for learning from multinomial data has recently been developed, giving predictive inferences in the form of lower and upper probabilities for a future observation. Apart from the past observations, no information on the sample space is assumed, so explicitly no assumptions are made on the number of possible categories. In this paper, we briefly present the general lower and upper probabilities corresponding to this model, and illustrate their properties via two examples taken from Walley’s paper [16], which introduced the imprecise Dirichlet model (IDM). As our approach is nonparametric, its applicability is more restricted. However, our inferences do not suffer from some disadvantages of the IDM.
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20080717
Source:
http://maths.dur.ac.uk/stats/people/fc/npi/
Coolen
Augustin.pdf
http://maths.dur.ac.uk/stats/people/fc/npi/
Coolen
Augustin.pdf
Minimize
Document Type:
text
Language:
en
Subjects:
data
data
Minimize
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.106.2486
http://maths.dur.ac.uk/stats/people/fc/npi/CoolenAugustin.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.106.2486
http://maths.dur.ac.uk/stats/people/fc/npi/CoolenAugustin.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
6.
Nonparametric predictive comparison of proportions
Open Access
Title:
Nonparametric predictive comparison of proportions
Author:
F. P. A. Coolen
;
P. Coolenschrijner
F. P. A. Coolen
;
P. Coolenschrijner
Minimize authors
Description:
We use the lower and upper predictive probabilities from
Coolen
[5] to compare future numbers of successes in Bernoulli trials for different groups. We consider both pairwise and multiple comparisons. These inferences are in terms of lower and upper probabilities that the number of successes in m future trials from one group exceeds the number o...
We use the lower and upper predictive probabilities from
Coolen
[5] to compare future numbers of successes in Bernoulli trials for different groups. We consider both pairwise and multiple comparisons. These inferences are in terms of lower and upper probabilities that the number of successes in m future trials from one group exceeds the number of successes in m future trials from another group, or such numbers from all other groups. We analyse these lower and upper probabilities via application to two data sets from the literature, and discuss the imprecision in relation to m.
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20080716
Source:
http://maths.dur.ac.uk/stats/people/fc/npi/propsrev.pdf
http://maths.dur.ac.uk/stats/people/fc/npi/propsrev.pdf
Minimize
Document Type:
text
Language:
en
Subjects:
Key Words ; Bernoulli trials ; Lower and upper probabilities ; Multiple comparisons ; Nonparametric predictive inference
Key Words ; Bernoulli trials ; Lower and upper probabilities ; Multiple comparisons ; Nonparametric predictive inference
Minimize
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.100.2793
http://maths.dur.ac.uk/stats/people/fc/npi/propsrev.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.100.2793
http://maths.dur.ac.uk/stats/people/fc/npi/propsrev.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
7.
Bayesian Reliability Demonstration
Open Access
Title:
Bayesian Reliability Demonstration
Author:
F. P. A. Coolen
;
P. Coolenschrijner
F. P. A. Coolen
;
P. Coolenschrijner
Minimize authors
Description:
This paper presents several main aspects of Bayesian reliability demonstration, together with a concise discussion of key contributions to this topic.
This paper presents several main aspects of Bayesian reliability demonstration, together with a concise discussion of key contributions to this topic.
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20140627
Source:
http://maths.dur.ac.uk/stats/people/fc/brdreview.pdf
http://maths.dur.ac.uk/stats/people/fc/brdreview.pdf
Minimize
Document Type:
text
Language:
en
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.7577
http://maths.dur.ac.uk/stats/people/fc/brdreview.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.7577
http://maths.dur.ac.uk/stats/people/fc/brdreview.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
8.
Nonparametric predictive inference for age replacement with a renewal argument. Quality and Reliablity Engineering
Open Access
Title:
Nonparametric predictive inference for age replacement with a renewal argument. Quality and Reliablity Engineering
Author:
P. Coolenschrijner
;
F. P. A. Coolen
P. Coolenschrijner
;
F. P. A. Coolen
Minimize authors
Description:
We consider an age replacement problem with cost function based on the renewal reward theorem. However, instead of assuming a known probability distribution for the lifetimes, we apply Hill’s assumption A (n) for predicting probabilities for the lifetime of a future item. Lower and upper bounds for the survival function of a future item are used...
We consider an age replacement problem with cost function based on the renewal reward theorem. However, instead of assuming a known probability distribution for the lifetimes, we apply Hill’s assumption A (n) for predicting probabilities for the lifetime of a future item. Lower and upper bounds for the survival function of a future item are used, resulting in upper and lower cost functions. Minimising these upper and lower cost functions to obtain the optimal age replacement times is simplified due to the special form of these functions. To discuss some features of our approach, we first study the consequences of using n equally spaced percentiles from a known distribution instead of n observed data. Secondly, we report on a simulation study where the lifetimes are simulated from known distributions, so that the optimal replacement times corresponding to our approach can be compared with the theoretical optimal replacement times. 1
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090806
Source:
http://maths.dur.ac.uk/stats/people/ps/arts2.pdf
http://maths.dur.ac.uk/stats/people/ps/arts2.pdf
Minimize
Document Type:
text
Language:
en
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.9847
http://maths.dur.ac.uk/stats/people/ps/arts2.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.9847
http://maths.dur.ac.uk/stats/people/ps/arts2.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
9.
Nonparametric adaptive opportunitybased age replacement strategies
Open Access
Title:
Nonparametric adaptive opportunitybased age replacement strategies
Author:
P. Coolenschrijner
;
F. P. A. Coolen
;
S. C. Shaw
P. Coolenschrijner
;
F. P. A. Coolen
;
S. C. Shaw
Minimize authors
Description:
We consider opportunitybased age replacement using nonparametric predictive inference (NPI) for the time to failure of a future unit. Based on n observed failure times, NPI provides lower and upper bounds for the survival function for the time to failure Xn+1 of a future unit, which lead to upper and lower cost functions, respectively, for oppo...
We consider opportunitybased age replacement using nonparametric predictive inference (NPI) for the time to failure of a future unit. Based on n observed failure times, NPI provides lower and upper bounds for the survival function for the time to failure Xn+1 of a future unit, which lead to upper and lower cost functions, respectively, for opportunitybased age replacement based on the renewal reward theorem. Optimal opportunitybased age replacement strategies for unit n+1 follow by minimising these cost functions. Following this strategy, unit n + 1 is correctively replaced upon failure, or preventively replaced upon the first opportunity after the optimal opportunitybased age replacement threshold. We study the effect of this replacement information for unit n + 1 on the optimal opportunitybased age replacement strategy for unit n + 2. We illustrate our method with examples and a simulation study. Our method is fully adaptive to available data, providing an alternative to the classical approach where the probability distribution of a unit’s time to failure is assumed to be known. We discuss the possible use of our method and compare it with the classical approach, where we conclude that in most situations our adaptive method performs very well, but that counterintuitive results can occur. Keywords: Opportunitybased age replacement; Nonparametric predictive inference; Renewal reward theorem.
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090806
Source:
http://maths.dur.ac.uk/stats/people/ps/opport3.pdf
http://maths.dur.ac.uk/stats/people/ps/opport3.pdf
Minimize
Document Type:
text
Language:
en
DDC:
519 Probabilities & applied mathematics
(computed)
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.72
http://maths.dur.ac.uk/stats/people/ps/opport3.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.72
http://maths.dur.ac.uk/stats/people/ps/opport3.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
10.
Nonparametric adaptive age replacement with a onecycle criterion
Open Access
Title:
Nonparametric adaptive age replacement with a onecycle criterion
Author:
P. Coolenschrijner
;
F. P. A. Coolen
P. Coolenschrijner
;
F. P. A. Coolen
Minimize authors
Description:
Age replacement of technical units has received much attention in the reliability literature over the last four decades. Mostly, the failure time distribution for the units is assumed to be known, and minimal costs per unit of time is used as optimality criterion, where renewal reward theory simplifies the mathematics involved but requires the a...
Age replacement of technical units has received much attention in the reliability literature over the last four decades. Mostly, the failure time distribution for the units is assumed to be known, and minimal costs per unit of time is used as optimality criterion, where renewal reward theory simplifies the mathematics involved but requires the assumption that the same process and replacement strategy continues over a very large (‘infinite’) period of time. Recently, there has been increasing attention to adaptive strategies for age replacement, taking into account the information from the process. Although renewal reward theory can still be used to provide an intuitively and mathematically attractive optimality criterion, it is more logical to use minimal costs per unit of time over a single cycle as optimality criterion for adaptive age replacement. In this paper, we first show that in the classical age replacement setting, with known failure time distribution with increasing hazard rate, the onecycle criterion leads to earlier replacement than the renewal reward criterion. Thereafter, we present adaptive age replacement with a onecycle criterion within the nonparametric predictive inferential framework. We study the performance of this approach via simulations, which are also used for comparisons with the use of the renewal reward criterion within the same statistical framework. Key words: Age replacement; nonparametric predictive inference; onecycle optimality criterion; renewal reward theorem. 1 1
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090806
Source:
http://maths.dur.ac.uk/stats/people/ps/ress1cyc.pdf
http://maths.dur.ac.uk/stats/people/ps/ress1cyc.pdf
Minimize
Document Type:
text
Language:
en
DDC:
519 Probabilities & applied mathematics
(computed)
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.6691
http://maths.dur.ac.uk/stats/people/ps/ress1cyc.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.6691
http://maths.dur.ac.uk/stats/people/ps/ress1cyc.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
Export Record
All Records
Export
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Adjust your hit list
Sort Your Results
Refine Search Result
More Options
Sort Your Results
Sort by:
Relevance
Author, ZA
Author, AZ
Title, AZ
Title, ZA
Date of publication, descending
Date of publication, ascending
Refine Search Result
Author
(21) The Pennsylvania State University CiteSeerX...
(20) Coolen, F. P. A.
(20) F. P. A. Coolen
(10) Alexov, A.
(10) Anderson, J.
(10) Beck, R.
(10) Bennema, P.
(10) Bregman, J.
(10) Breitling, F.
(10) Ciardi, B.
(10) Coolen, A.
(10) Corstanje, A.
(10) Falcke, H.
(10) Frieswijk, W.
(10) Grit, T.
(10) Hoeft, M.
(10) Horneffer, A.
(10) Iacobelli, M.
(10) Karastergiou, A.
(10) Kramer, M.
(10) Kuniyoshi, M.
(10) Kuper, G.
(10) Maat, P.
(10) Macario, G.
(10) Mann, G.
(10) Markoff, S.
(10) McKayBukowski, D.
(10) Mevius, M.
(10) Munk, H.
(10) Nelles, A.
(10) Nijboer, R.
(10) Overeem, R.
(10) Paas, H.
(10) PandeyPommier, M.
(10) Pizzo, R.
(10) Schellart, P.
(10) Sluman, J.
(10) Smirnov, O.
(10) Sobey, C.
(10) Steinmetz, M.
(10) Swinbank, J.
(10) Wucknitz, O.
(10) Yatawatta, S.
(10) Zarka, P.
(9) Best, P.
(9) Brentjens, M.
(9) Broderick, J.
(9) Ferrari, C.
(9) Heald, G.
(9) Schoenmakers, A.
(9) Tang, Y.
(9) Tasse, C.
(9) Thoudam, S.
(9) Vermaas, N.
(9) Vermeulen, R.
(9) Vocks, C.
(9) de Geus, E.
(9) de Jong, A.
(9) de Vos, M.
(9) ter Veen, S.
(8) Bernardi, G.
(8) CoolenSchrijner, P.
(8) Vogt, C.
(8) Zensus, A.
(8) van Leeuwen, J.
(7) Avruch, I. M.
(7) Bell, M. E.
(7) Bentum, M. J.
(7) Fallows, R.
(7) Garrett, M. A.
(7) Hamaker, J. P.
(7) Hessels, J. W. T.
(7) Juette, E.
(7) Kondratiev, V. I.
(7) McKean, J. P.
(7) MillerJones, J. C. A.
(7) Mulcahy, D. D.
(7) Norden, M.
(7) Orru, E.
(7) P. Coolenschrijner
(7) Polatidis, A.
(7) Romein, J. W.
(7) Stappers, B. W.
(7) Wijnholds, S. J.
(7) Wise, M. W.
(7) de Gasperin, F.
(7) van Nieuwpoort, R.
(6) Anderson, K.
(6) Arts, M.
(6) Asgekar, A.
(6) Braun, R.
(6) Coenen, T.
(6) Coolen, A C C
(6) Damstra, S.
(6) Davies, O.
(6) Dijkstra, K.
(6) Donker, P.
(6) Doorduin, A.
(6) Dromer, J.
(6) Drost, M.
Author:
Subject
(8) instrumentation interferometers
(5) reionization
(4) astroparticle physics
(4) dark ages
(4) first stars
(4) nonparametric predictive inference
(4) radio continuum general
(4) telescopes
(3) articles
(3) methods data analysis
(3) radio lines general
(2) article
(2) astrophysics instrumentation and methods for...
(2) ddc 510
(2) imprecise probabilities
(2) lower and upper probabilities
(2) lower probability
(2) mathematical physics and mathematics
(2) multinomial data
(2) representation of sub populations
(2) sdv neu life sciences neurons and cognition
(2) sonderforschungsbereich 386
(1) a n
(1) and comment on the possibility of introducing...
(1) archéologie
(1) astro ph im
(1) astrophysics high energy astrophysical phenomena
(1) based on heterogeneous interacting agents
(1) bayes linear methods
(1) bernoulli trials
(1) binomial distribution
(1) called the exponential connection
(1) circular data
(1) clinical study
(1) condensed matter disordered systems and neural...
(1) condensed matter other condensed matter
(1) data
(1) ddc 310
(1) ddc 500
(1) ddc 520
(1) ddc 530
(1) digital sky survey
(1) dk atira pure subjectarea asjc 2600 2610
(1) dk atira pure subjectarea asjc 2600 2611
(1) dk atira pure subjectarea asjc 2600 2613
(1) dk atira pure subjectarea asjc 3100
(1) dk atira pure subjectarea asjc 3100 3109
(1) doaj gynecology and obstetrics
(1) doaj health sciences
(1) doaj medicine general
(1) exchangeability
(1) expert knowledge
(1) exponential distribution
(1) first stars telescopes ray air showers...
(1) gamma distribution
(1) general
(1) grouped data
(1) high redshift
(1) i e in parallel in contrast to the more...
(1) imprecise dirichlet model
(1) intergalactic medium
(1) interplanetary scintillation
(1) interval probability
(1) isi archaeology
(1) key words
(1) known number of categories
(1) lcc gynecology and obstetrics
(1) lcc medical emergencies critical care intensive...
(1) lcc medicine
(1) lcc r
(1) lcc rc86 88 9
(1) lcc rg1 991
(1) mathematical physics
(1) methods data analysis instrumentation...
(1) modelling and simulation
(1) monte carlo simulations
(1) multinominal data
(1) multiple comparisons
(1) normal distribution
(1) observations
(1) ost history archaeology
(1) partition testing
(1) physics and astronomy all
(1) physics optics
(1) poisson distribution
(1) probability wheel
(1) probe wmap
(1) qa276 mathematical statistics
(1) quantum physics
(1) radio continuum general radio lines general...
(1) radio lines
(1) ray air showers
(1) self calibration
(1) software testing theory
(1) statistical and nonlinear physics
(1) statistics and probability
(1) this paper reviews some of the phenomenological...
(1) understanding radio polarimetry
(1) we first present an exact solution of the one...
(1) we present the construction of an infinite...
Subject:
Dewey Decimal Classification (DDC)
(8) Astronomy [52*]
(7) Mathematics [51*]
(4) Statistics [31*]
(3) Logic [16*]
(2) Medicine & health [61*]
(1) Education [37*]
(1) Science [50*]
(1) Physics [53*]
(1) Sports, games & entertainment [79*]
Dewey Decimal Classification (DDC):
Year of Publication
(20) 2013
(11) 2009
(7) 2001
(7) 2008
(6) 2006
(5) 2012
(4) 2010
(3) 2004
(3) 2005
(2) 2011
(2) 2014
(2) 2015
(1) 1996
(1) 2002
(1) 2007
Year of Publication:
Content Provider
(21) CiteSeerX
(9) Durham Univ.: Research Online
(5) HighWire Press
(5) London King's College: Research Portal
(4) ArXiv.org
(4) RePEc.org
(3) Manchester Univ.: eScholar Services
(2) HAL  Hyper Article en Ligne
(2) Curtin Univ. Tech.: espace@Curtin
(2) DOAJ Articles
(2) Hindawi Publishing Corporation
(2) Munich LMU: Open Access
(2) Groningen Univ.
(2) Oxford Univ.: Research Archive (ORA)
(2) Bath Univ.: OPus
(1) STFC (United Kingdom)
(1) CERN (Switzerland)
(1) Göteborg Chalmers Univ. of Technology
(1) Joint Inst. for Nuclear Research: JINR Document...
(1) Kent Univ.
(1) Leicester Univ.
(1) OpenEdition
(1) PubMed Central
(1) Bielefeld Univ.: Publications
(1) Bochum Univ. (RUB): Campus Research Bibliography
(1) Twente Univ.: Publications
(1) EconStor
Content Provider:
Language
(52) English
(27) Unknown
Language:
Document Type
(36) Article, Journals
(33) Text
(6) Reports, Papers, Lectures
(4) Unknown
Document Type:
Access
(44) Unknown
(35) Open Access
Access:
More Options
»
Search History
»
Get RSS Feed
»
Get ATOM Feed
»
Email this Search
»
Save Search
»
Browsing
»
Search Plugin
Further result pages
Results:
1

2

3

4

5

6

7

8
Next »
New Search »
Currently in BASE: 72,247,077 Documents of 3,474
Content Sources
About BASE

Contact

BASE Lab

Imprint
© 20042015 by
Bielefeld University Library
Search powered by
Solr
&
VuFind
.
Suggest Repository
BASE Interfaces
Currently in BASE: 72,247,077 Documents of 3,474 Content Sources
http://www.basesearch.net