ELIZA

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239) We, therefore, recommend its use when continuous-time data are available.

--- ABSTRACT ---

PMID:24114068 DOI:10.1093/imammb/dqt021

2015 Mathematical medicine and biology : a journal of the IMA

* Efficient parameter estimation for models of healthcare-associated pathogen transmission in discrete and continuous time.

- We describe two novel Markov chain Monte Carlo approaches to computing estimates of parameters concerned with healthcare-associated infections. The first approach frames the discrete time, patient level, hospital transmission model as a Bayesian network, and exploits this framework to improve greatly on the computational efficiency of estimation compared with existing programs. The second approach is in continuous time and shares the same computational advantages. Both methods have been implemented in programs that are available from the authors. We use these programs to show that time discretization can lead to statistical bias in the underestimation of the rate of transmission of pathogens. We show that the continuous implementation has similar running time to the discrete implementation, has better Markov chain mixing properties, and eliminates the potential statistical bias. We, therefore, recommend its use when continuous-time data are available.

--- ABSTRACT END ---

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--- WordNet output for data --- =>データ, 資料 Overview of noun data The noun data has 1 sense (first 1 from tagged texts) 1. (76) data, information -- (a collection of facts from which conclusions may be drawn; "statistical data") Overview of noun datum The noun datum has 1 sense (first 1 from tagged texts) 1. (5) datum, data point -- (an item of factual information derived from measurement or research) --- WordNet end ---