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1. |
Record Nr. |
UNINA9910780276703321 |
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Autore |
Davies John Keith |
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Titolo |
Beyond Pluto : exploring the outer limits of the solar system / / John Davies [[electronic resource]] |
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Pubbl/distr/stampa |
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Cambridge : , : Cambridge University Press, , 2001 |
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ISBN |
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1-107-12239-2 |
1-107-40261-1 |
0-511-30335-1 |
0-511-53609-7 |
0-511-04741-X |
1-280-43020-6 |
0-511-17384-9 |
0-511-15308-2 |
9786610430208 |
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Descrizione fisica |
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1 online resource (xii, 233 pages) : digital, PDF file(s) |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Title from publisher's bibliographic system (viewed on 05 Oct 2015). |
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Nota di contenuto |
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Prologue -- The edge of the solar system -- The Centaurs -- The mystery of the short-period comets -- Shooting in the dark -- Deeper and deeper -- Sorting out the dynamics -- What are little planets made of? -- Numbers and sizes -- Things that go bump in the dark -- Dust and discs -- Where do we go from here? -- Will we ever get our names right? -- Appendix 1: Dramatis personae -- Appendix 2: Guidelines for minor planet names -- Index. |
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Sommario/riassunto |
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This book was originally published in 2001. In the ten years preceding publication, the known solar system more than doubled in size. For the first time in almost two centuries an entirely new population of planetary objects was found. This 'Kuiper Belt' of minor planets beyond Neptune revolutionised our understanding of the solar system's formation and finally explained the origin of the enigmatic outer planet Pluto. This is the fascinating story of how theoretical physicists decided |
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that there must be a population of unknown bodies beyond Neptune and how a small band of astronomers set out to find them. What they discovered was a family of ancient planetesimals whose orbits and physical properties were far more complicated than anyone expected. We follow the story of this discovery, and see how astronomers, theoretical physicists and one incredibly dedicated amateur observer came together to explore the frozen boundary of the solar system. |
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2. |
Record Nr. |
UNINA9910557660803321 |
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Autore |
Nielsen Jens Perch |
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Titolo |
Machine Learning in Insurance |
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Pubbl/distr/stampa |
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Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
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Descrizione fisica |
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1 online resource (260 p.) |
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Soggetti |
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History of engineering and technology |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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Machine learning is a relatively new field, without a unanimous definition. In many ways, actuaries have been machine learners. In both pricing and reserving, but also more recently in capital modelling, actuaries have combined statistical methodology with a deep understanding of the problem at hand and how any solution may affect the company and its customers. One aspect that has, perhaps, not been so well developed among actuaries is validation. Discussions among actuaries' "preferred methods" were often without solid scientific arguments, including validation of the case at hand. Through this collection, we aim to promote a good practice of machine learning in insurance, considering the following three key issues: a) who is the client, or sponsor, or otherwise interested real-life target of the study? b) The reason for working with a particular data set and a clarification of the available extra knowledge, that we also call prior knowledge, |
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besides the data set alone. c) A mathematical statistical argument for the validation procedure. |
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