The linear birthdeath process: An inferential retrospective
cam.issuedOnline | 2019-02-01 | |
dc.contributor.author | Tavaré, S | |
dc.date.accessioned | 2019-01-15T00:30:57Z | |
dc.date.available | 2019-01-15T00:30:57Z | |
dc.date.issued | 2018 | |
dc.description.abstract | <jats:title>Abstract</jats:title><jats:p> In this paper we provide an introduction to statistical inference for the classical linear birth‒death process, focusing on computational aspects of the problem in the setting of discretely observed processes. The basic probabilistic properties are given in Section 2, focusing on computation of the transition functions. This is followed by a brief discussion of simulation methods in Section 3, and of frequentist methods in Section 4. Section 5 is devoted to Bayesian methods, from rejection sampling to Markov chain Monte Carlo and approximate Bayesian computation. In Section 6 we consider the time-inhomogeneous case. The paper ends with a brief discussion in Section 7. </jats:p> | |
dc.identifier.doi | 10.17863/CAM.35301 | |
dc.identifier.eissn | 1475-6064 | |
dc.identifier.issn | 0001-8678 | |
dc.identifier.uri | https://www.repository.cam.ac.uk/handle/1810/287981 | |
dc.language.iso | eng | |
dc.publisher | Cambridge University Press (CUP) | |
dc.publisher.url | http://dx.doi.org/10.1017/apr.2018.84 | |
dc.subject | Approximate Bayesian computation | |
dc.subject | Markov chain Monte Carlo | |
dc.subject | estimation | |
dc.title | The linear birthdeath process: An inferential retrospective | |
dc.type | Article | |
dcterms.dateAccepted | 2018-10-04 | |
prism.endingPage | 269 | |
prism.issueIdentifier | A | |
prism.publicationDate | 2018 | |
prism.publicationName | Advances in Applied Probability | |
prism.startingPage | 253 | |
prism.volume | 50 | |
rioxxterms.licenseref.startdate | 2018-12-01 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.type | Journal Article/Review | |
rioxxterms.version | AM | |
rioxxterms.versionofrecord | 10.1017/apr.2018.84 |
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