kwic
observed
observed
right
off
ON
391
1
100
500
strict
ELIZA cgi-bash version rev. 1.90
- Medical English LInking keywords finder for the PubMed Zipped Archive (ELIZA) -
return
kwic search for observed out of >500 occurrences
392826 occurrences (No.45 in the rank) during 5 years in the PubMed. [no cache] 500 found
--- ABSTRACT ---
--- ABSTRACT END ---
[frequency of next (right) word to observed]
392826 occurrences (No.45 in the rank) during 5 years in the PubMed. [no cache] 500 found
| 391) A graphical model is built to represent how observed data are generated from statistical models with unknown parameters and how those parameters are related to quantities of interest for decision making. |
| PMID:23886677 DOI:10.1177/0272989X13493143 |
| 2015 Medical decision making : an international journal of the Society for Medical Decision Making |
| * Calibration of complex models through Bayesian evidence synthesis: a demonstration and tutorial. |
| - Decision-analytic models must often be informed using data that are only indirectly related to the main model parameters. The authors outline how to implement a Bayesian synthesis of diverse sources of evidence to calibrate the parameters of a complex model. A graphical model is built to represent how observed data are generated from statistical models with unknown parameters and how those parameters are related to quantities of interest for decision making. This forms the basis of an algorithm to estimate a posterior probability distribution, which represents the updated state of evidence for all unknowns given all data and prior beliefs. This process calibrates the quantities of interest against data and, at the same time, propagates all parameter uncertainties to the results used for decision making. To illustrate these methods, the authors demonstrate how a previously developed Markov model for the progression of human papillomavirus (HPV-16) infection was rebuilt in a Bayesian framework. Transition probabilities between states of disease severity are inferred indirectly from cross-sectional observations of prevalence of HPV-16 and HPV-16-related disease by age, cervical cancer incidence, and other published information. Previously, a discrete collection of plausible scenarios was identified but with no further indication of which of these are more plausible. Instead, the authors derive a Bayesian posterior distribution, in which scenarios are implicitly weighted according to how well they are supported by the data. In particular, we emphasize the appropriate choice of prior distributions and checking and comparison of fitted models. |
[
right
kwic]
(1)155  in |
(9)9 on |
(17)4 a |
(25)2 data |
| (2)48 *null* | (10)8 by |
(18)4 during |
(26)2 experimentally |
| (3)36 that |
(11)8 to |
(19)4 the |
(27)2 reduction |
| (4)19 for |
(12)8 when |
(20)3 variation |
(28)2 significant |
| (5)18 at |
(13)6 among |
(21)2 as |
(29)2 through |
| (6)16 between |
(14)6 following |
(22)2 changes |
(30)2 throughout |
| (7)11 with |
(15)5 after |
(23)2 clinical |
(31)2 up |
| (8)9 and |
(16)5 from |
(24)2 compared |
(32)2 using |
add keyword
--- WordNet output for observed ---