[Pomp-commits] r729 - pkg/pomp/inst/doc

[noreply at r-forge.r-project.org]

Author: kingaa
Date: 2012-06-12 18:43:01 +0200 (Tue, 12 Jun 2012)
New Revision: 729

Modified:
   pkg/pomp/inst/doc/pomp.bib
Log:
- update bibliography database


Modified: pkg/pomp/inst/doc/pomp.bib
===================================================================
--- pkg/pomp/inst/doc/pomp.bib	2012-06-12 16:25:22 UTC (rev 728)
+++ pkg/pomp/inst/doc/pomp.bib	2012-06-12 16:43:01 UTC (rev 729)
@@ -417,6 +417,46 @@
   timestamp = {2008.08.13}
 }
 
+ at ARTICLE{Knape2011,
+  author = {Knape, Jonas and de Valpine, Perry},
+  title = {Fitting complex population models by combining particle filters with
+	Markov chain Monte Carlo},
+  journal = {Ecology},
+  year = {2011},
+  volume = {93},
+  pages = {256--263},
+  number = {2},
+  month = oct,
+  __markedentry = {[kingaa:]},
+  abstract = {We show how a recent framework combining Markov chain Monte Carlo
+	(MCMC) with particle filters (PFMCMC) may be used to estimate population
+	state-space models. With the purpose of utilizing the strengths of
+	each method, PFMCMC explores hidden states by particle filters, while
+	process and observation parameters are estimated using an MCMC algorithm.
+	PFMCMC is exemplified by analyzing time series data on a red kangaroo
+	(Macropus rufus) population in New South Wales, Australia, using
+	MCMC over model parameters based on an adaptive Metropolis-Hastings
+	algorithm. We fit three population models to these data; a density-dependent
+	logistic diffusion model with environmental variance, an unregulated
+	stochastic exponential growth model, and a random-walk model. Bayes
+	factors and posterior model probabilities show that there is little
+	support for density dependence and that the random-walk model is
+	the most parsimonious model. The particle filter Metropolis-Hastings
+	algorithm is a brute-force method that may be used to fit a range
+	of complex population models. Implementation is straightforward and
+	less involved than standard MCMC for many models, and marginal densities
+	for model selection can be obtained with little additional effort.
+	The cost is mainly computational, resulting in long running times
+	that may be improved by parallelizing the algorithm.},
+  booktitle = {Ecology},
+  doi = {10.1890/11-0797.1},
+  file = {:Knape2011.pdf:PDF},
+  issn = {0012-9658},
+  owner = {kingaa},
+  publisher = {Ecological Society of America},
+  timestamp = {2012.06.01}
+}
+
 @INCOLLECTION{Liu2001b,
   author = {Liu, J and West, M.},
   title = {{C}ombining {P}arameter and {S}tate {E}stimation in {S}imulation-{B}ased