Approximate Bayesian computation schemes for parameter inference of discrete stochastic models using simulated likelihood density

Qian Qian Wu; Kate Amanda Smith-Miles; Tianhai Tian

doi:10.1186/1471-2105-15-S12-S3

Approximate Bayesian computation schemes for parameter inference of discrete stochastic models using simulated likelihood density

Qian Qian Wu, Kate Amanda Smith-Miles, Tianhai Tian

School of Mathematics

Research output: Contribution to journal › Article › Research › peer-review

22 Citations (Scopus)

Original language	English
Pages (from-to)	1 - 10
Number of pages	10
Journal	BMC Bioinformatics
Volume	15
DOIs	https://doi.org/10.1186/1471-2105-15-S12-S3
Publication status	Published - 2014

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10.1186/1471-2105-15-S12-S3

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title = "Approximate Bayesian computation schemes for parameter inference of discrete stochastic models using simulated likelihood density",

author = "Wu, \{Qian Qian\} and Smith-Miles, \{Kate Amanda\} and Tianhai Tian",

year = "2014",

doi = "10.1186/1471-2105-15-S12-S3",

language = "English",

volume = "15",

pages = "1 -- 10",

journal = "BMC Bioinformatics",

issn = "1471-2105",

publisher = "BioMed Central",

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T1 - Approximate Bayesian computation schemes for parameter inference of discrete stochastic models using simulated likelihood density

AU - Wu, Qian Qian

AU - Smith-Miles, Kate Amanda

AU - Tian, Tianhai

PY - 2014

Y1 - 2014

UR - https://www.scopus.com/pages/publications/84961588782

U2 - 10.1186/1471-2105-15-S12-S3

DO - 10.1186/1471-2105-15-S12-S3

M3 - Article

SN - 1471-2105

VL - 15

SP - 1

EP - 10

JO - BMC Bioinformatics

JF - BMC Bioinformatics

ER -

Approximate Bayesian computation schemes for parameter inference of discrete stochastic models using simulated likelihood density

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Approximate Bayesian computation schemes for parameter inference of discrete stochastic models using simulated likelihood density

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Stochastic modelling of telomere length regulation in ageing research

Stochastic modelling of genetic regulatory networks with burst process

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