Computing Cumulative Rewards Using Fast Adaptive Uniformisation View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2013

AUTHORS

Frits Dannenberg , Ernst Moritz Hahn , Marta Kwiatkowska

ABSTRACT

The computation of transient probabilities for continuous-time Markov chains often employs uniformisation, also known as the Jensen’s method. The fast adaptive uniformisation method introduced by Mateescu approximates the probability by neglecting insignificant states, and has proven to be effective for quantitative analysis of stochastic models arising in chemical and biological applications. However, this method has only been formulated for the analysis of properties at a given point of time t. In this paper, we extend fast adaptive uniformisation to handle expected reward properties which reason about the model behaviour until time t, for example, the expected number of chemical reactions that have occurred until t. To show the feasibility of the approach, we integrate the method into the probabilistic model checker PRISM and apply it to a range of biological models, demonstrating superior performance compared to existing techniques. More... »

PAGES

33-49

Book

TITLE

Computational Methods in Systems Biology

ISBN

978-3-642-40707-9
978-3-642-40708-6

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-40708-6_4

DOI

http://dx.doi.org/10.1007/978-3-642-40708-6_4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1028536457


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Computer Science, University of Oxford, UK", 
          "id": "http://www.grid.ac/institutes/grid.4991.5", 
          "name": [
            "Department of Computer Science, University of Oxford, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Dannenberg", 
        "givenName": "Frits", 
        "id": "sg:person.010040156213.45", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010040156213.45"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "State Key Laboratory of Computer Science, ISCAS, China", 
          "id": "http://www.grid.ac/institutes/grid.458446.f", 
          "name": [
            "Department of Computer Science, University of Oxford, UK", 
            "State Key Laboratory of Computer Science, ISCAS, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hahn", 
        "givenName": "Ernst Moritz", 
        "id": "sg:person.011246511367.12", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011246511367.12"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Computer Science, University of Oxford, UK", 
          "id": "http://www.grid.ac/institutes/grid.4991.5", 
          "name": [
            "Department of Computer Science, University of Oxford, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kwiatkowska", 
        "givenName": "Marta", 
        "id": "sg:person.011375012273.39", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011375012273.39"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2013", 
    "datePublishedReg": "2013-01-01", 
    "description": "The computation of transient probabilities for continuous-time Markov chains often employs uniformisation, also known as the Jensen\u2019s method. The fast adaptive uniformisation method introduced by Mateescu approximates the probability by neglecting insignificant states, and has proven to be effective for quantitative analysis of stochastic models arising in chemical and biological applications. However, this method has only been formulated for the analysis of properties at a given point of time t. In this paper, we extend fast adaptive uniformisation to handle expected reward properties which reason about the model behaviour until time t, for example, the expected number of chemical reactions that have occurred until t. To show the feasibility of the approach, we integrate the method into the probabilistic model checker PRISM and apply it to a range of biological models, demonstrating superior performance compared to existing techniques.", 
    "editor": [
      {
        "familyName": "Gupta", 
        "givenName": "Ashutosh", 
        "type": "Person"
      }, 
      {
        "familyName": "Henzinger", 
        "givenName": "Thomas A.", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-642-40708-6_4", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-3-642-40707-9", 
        "978-3-642-40708-6"
      ], 
      "name": "Computational Methods in Systems Biology", 
      "type": "Book"
    }, 
    "keywords": [
      "continuous-time Markov chain", 
      "stochastic model", 
      "Markov chain", 
      "transient probabilities", 
      "insignificant states", 
      "analysis of properties", 
      "time t.", 
      "time t", 
      "model checker PRISM", 
      "probabilistic model checker PRISM", 
      "uniformisation", 
      "model behavior", 
      "cumulative reward", 
      "biological models", 
      "probability", 
      "superior performance", 
      "Jensen's method", 
      "computation", 
      "model", 
      "chemical reactions", 
      "properties", 
      "T.", 
      "quantitative analysis", 
      "point", 
      "applications", 
      "approach", 
      "reward properties", 
      "technique", 
      "number", 
      "state", 
      "analysis", 
      "performance", 
      "biological applications", 
      "behavior", 
      "chain", 
      "range", 
      "feasibility", 
      "prism", 
      "reasons", 
      "reward", 
      "chemicals", 
      "reaction", 
      "method", 
      "example", 
      "paper"
    ], 
    "name": "Computing Cumulative Rewards Using Fast Adaptive Uniformisation", 
    "pagination": "33-49", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1028536457"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-642-40708-6_4"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-642-40708-6_4", 
      "https://app.dimensions.ai/details/publication/pub.1028536457"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-05-20T07:49", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/chapter/chapter_68.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-642-40708-6_4"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-40708-6_4'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-40708-6_4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-40708-6_4'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-40708-6_4'


 

This table displays all metadata directly associated to this object as RDF triples.

128 TRIPLES      23 PREDICATES      71 URIs      64 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-642-40708-6_4 schema:about anzsrc-for:01
2 anzsrc-for:0104
3 schema:author Ndd3977a32d1343d7b502cb190d43c1a4
4 schema:datePublished 2013
5 schema:datePublishedReg 2013-01-01
6 schema:description The computation of transient probabilities for continuous-time Markov chains often employs uniformisation, also known as the Jensen’s method. The fast adaptive uniformisation method introduced by Mateescu approximates the probability by neglecting insignificant states, and has proven to be effective for quantitative analysis of stochastic models arising in chemical and biological applications. However, this method has only been formulated for the analysis of properties at a given point of time t. In this paper, we extend fast adaptive uniformisation to handle expected reward properties which reason about the model behaviour until time t, for example, the expected number of chemical reactions that have occurred until t. To show the feasibility of the approach, we integrate the method into the probabilistic model checker PRISM and apply it to a range of biological models, demonstrating superior performance compared to existing techniques.
7 schema:editor N8d7b5382698e46d892f8b1cf27285637
8 schema:genre chapter
9 schema:inLanguage en
10 schema:isAccessibleForFree true
11 schema:isPartOf N7186bf48163e4a9e8ed1fb2f6d6cc068
12 schema:keywords Jensen's method
13 Markov chain
14 T.
15 analysis
16 analysis of properties
17 applications
18 approach
19 behavior
20 biological applications
21 biological models
22 chain
23 chemical reactions
24 chemicals
25 computation
26 continuous-time Markov chain
27 cumulative reward
28 example
29 feasibility
30 insignificant states
31 method
32 model
33 model behavior
34 model checker PRISM
35 number
36 paper
37 performance
38 point
39 prism
40 probabilistic model checker PRISM
41 probability
42 properties
43 quantitative analysis
44 range
45 reaction
46 reasons
47 reward
48 reward properties
49 state
50 stochastic model
51 superior performance
52 technique
53 time t
54 time t.
55 transient probabilities
56 uniformisation
57 schema:name Computing Cumulative Rewards Using Fast Adaptive Uniformisation
58 schema:pagination 33-49
59 schema:productId N1e4049f1537b4dd58a9c32a159f5bbff
60 N5a4c958f0deb4d148dac5edd1247c203
61 schema:publisher N7ec01cbbd10b424ab465d71b78eb467f
62 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028536457
63 https://doi.org/10.1007/978-3-642-40708-6_4
64 schema:sdDatePublished 2022-05-20T07:49
65 schema:sdLicense https://scigraph.springernature.com/explorer/license/
66 schema:sdPublisher Necf7010faf404c08804eed142020735e
67 schema:url https://doi.org/10.1007/978-3-642-40708-6_4
68 sgo:license sg:explorer/license/
69 sgo:sdDataset chapters
70 rdf:type schema:Chapter
71 N1974ecdd948b42bda5d88b42db230618 schema:familyName Gupta
72 schema:givenName Ashutosh
73 rdf:type schema:Person
74 N1e4049f1537b4dd58a9c32a159f5bbff schema:name dimensions_id
75 schema:value pub.1028536457
76 rdf:type schema:PropertyValue
77 N211680c24e6d4811a67abcc51730013a rdf:first N91c9d98216ab4a97b84caace1243390d
78 rdf:rest rdf:nil
79 N5a4c958f0deb4d148dac5edd1247c203 schema:name doi
80 schema:value 10.1007/978-3-642-40708-6_4
81 rdf:type schema:PropertyValue
82 N7186bf48163e4a9e8ed1fb2f6d6cc068 schema:isbn 978-3-642-40707-9
83 978-3-642-40708-6
84 schema:name Computational Methods in Systems Biology
85 rdf:type schema:Book
86 N7d166cf96e014715b9f5677030e1ffa7 rdf:first sg:person.011246511367.12
87 rdf:rest Nb61273ba219f4053ac2d386226ec0431
88 N7ec01cbbd10b424ab465d71b78eb467f schema:name Springer Nature
89 rdf:type schema:Organisation
90 N8d7b5382698e46d892f8b1cf27285637 rdf:first N1974ecdd948b42bda5d88b42db230618
91 rdf:rest N211680c24e6d4811a67abcc51730013a
92 N91c9d98216ab4a97b84caace1243390d schema:familyName Henzinger
93 schema:givenName Thomas A.
94 rdf:type schema:Person
95 Nb61273ba219f4053ac2d386226ec0431 rdf:first sg:person.011375012273.39
96 rdf:rest rdf:nil
97 Ndd3977a32d1343d7b502cb190d43c1a4 rdf:first sg:person.010040156213.45
98 rdf:rest N7d166cf96e014715b9f5677030e1ffa7
99 Necf7010faf404c08804eed142020735e schema:name Springer Nature - SN SciGraph project
100 rdf:type schema:Organization
101 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
102 schema:name Mathematical Sciences
103 rdf:type schema:DefinedTerm
104 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
105 schema:name Statistics
106 rdf:type schema:DefinedTerm
107 sg:person.010040156213.45 schema:affiliation grid-institutes:grid.4991.5
108 schema:familyName Dannenberg
109 schema:givenName Frits
110 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010040156213.45
111 rdf:type schema:Person
112 sg:person.011246511367.12 schema:affiliation grid-institutes:grid.458446.f
113 schema:familyName Hahn
114 schema:givenName Ernst Moritz
115 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011246511367.12
116 rdf:type schema:Person
117 sg:person.011375012273.39 schema:affiliation grid-institutes:grid.4991.5
118 schema:familyName Kwiatkowska
119 schema:givenName Marta
120 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011375012273.39
121 rdf:type schema:Person
122 grid-institutes:grid.458446.f schema:alternateName State Key Laboratory of Computer Science, ISCAS, China
123 schema:name Department of Computer Science, University of Oxford, UK
124 State Key Laboratory of Computer Science, ISCAS, China
125 rdf:type schema:Organization
126 grid-institutes:grid.4991.5 schema:alternateName Department of Computer Science, University of Oxford, UK
127 schema:name Department of Computer Science, University of Oxford, UK
128 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...