Description Usage Arguments Details Value Note Author(s) References Examples
Returns the normalising constant, given posterior model densities, q1(.), normalised densities, q2(.) and their ratios, l(.), from both unnormailised (w1
) and normalised samples (w2
) of parameter values. All three of these input measures can be obtained from each sample using the q1q2l
function.
1 |
w1 |
A |
w2 |
A |
r0 |
Starting value for the calculation of the normalising constant. |
tol |
Tolerance level for convergence. |
verbose |
Print sample sizes of |
Provides an iterative solution to estimate the normalising constant, following equation (4.1) in Meng and Wong (1996). We adapted their equation slightly to deal with overflow (exponentiating large numbers).
See Details.
Adaption for overflow based on method suggested by Jon Forster.
Guy J. Abel
Abel, G.J., Bijak, J., Forster, J.J., Raymer J., Smith P.W.F. and Wong, J.S.T. (2013) Integrating uncertainty in time series population forecasts: An illustration using a simple projection model. Demographic Research. 29 43 1187-1226 doi:10.4054/DemRes.2013.29.43
Meng, X.-L., & Wong, W. H. (1996). Simulating Ratios of Normalizing Constants via a Simple Identity: A Theoretical Exploration. Statistica Sinica, 6, 831-860.
1 2 3 4 5 6 | ## Not run:
# demo example with constant variance models for differenced growth rate
# of England and Wales population as used in Abel et. al. (2013)
demo("cv_bma", "tsbridge")
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.