[Pomp-commits] r904 - in www: content vignettes
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Fri Mar 21 18:41:10 CET 2014
Author: kingaa
Date: 2014-03-21 18:41:09 +0100 (Fri, 21 Mar 2014)
New Revision: 904
Modified:
www/content/about.htm
www/content/refs.htm
www/content/refs.tex
www/vignettes/pomp.bib
Log:
- add some new references
Modified: www/content/about.htm
===================================================================
--- www/content/about.htm 2014-03-21 15:46:30 UTC (rev 903)
+++ www/content/about.htm 2014-03-21 17:41:09 UTC (rev 904)
@@ -11,16 +11,18 @@
<li>the particle Markov chain Monte Carlo method of Andrieu et al. (2010),</li>
+ <li>approximate Bayesian computation (ABC; Toni et al. 2009) </li>
+
<li>the iterated filtering method of Ionides, Breto, & King (2006),</li>
- <li>the nonlinear forecasting method of Ellner & Kendall,</li>
+ <li>probe-matching methods (e.g., Kendall et al. 1999)</li>
- <li>probe-matching methods (e.g., Kendall et al. 1999), and</li>
+ <li>the nonlinear forecasting method of Ellner & Kendall,and</li>
<li>power-spectrum-matching methods of Reuman et al. (2006).</li>
</ul>
-<p>Simple worked examples are provided in <a href="http://cran.at.r-project.org/web/packages/pomp/vignettes/">vignettes</a> and in the <code>examples</code> directory of the installed package.</p>
+<p>Simple worked examples are provided in <a href="./index.php?nav=vignettes">vignettes</a> and in the <code>examples</code> directory of the installed package.</p>
<p>Future support for a variety of other algorithms is envisioned. A working group of the <a href="http://nceas.ucsb.edu">National Center for Ecological Analysis and Synthesis (NCEAS)</a>, "Inference for Mechanistic Models", is currently implementing additional methods for this package.</p>
Modified: www/content/refs.htm
===================================================================
--- www/content/refs.htm 2014-03-21 15:46:30 UTC (rev 903)
+++ www/content/refs.htm 2014-03-21 17:41:09 UTC (rev 904)
@@ -1,63 +1,73 @@
-<h2><a name="SECTIONREF">References</a></h2>
+<h3><a name="SECTIONREF" id="SECTIONREF">References</a></h3>
<dl compact>
- <dt><a name="Andrieu2010">Andrieu, C., A. Doucet, and R. Holenstein. 2010.</a></dt>
+ <dt><a name="Andrieu2010" id="Andrieu2010">Andrieu, C., A. Doucet, and R. Holenstein. 2010.</a></dt>
<dd>Particle Markov chain Monte Carlo methods.<br>
Journal of the Royal Statistical Society, Series B, <b>72</b>:269-342.<br></dd>
- <dt><a name="Arulampalam2002">Arulampalam, M. S., S. Maskell, N. Gordon, and T. Clapp. 2002.</a></dt>
+ <dt><a name="Arulampalam2002" id="Arulampalam2002">Arulampalam, M. S., S. Maskell, N. Gordon, and T. Clapp. 2002.</a></dt>
<dd>A Tutorial on Particle Filters for Online Nonlinear, Non-Gaussian Bayesian Tracking.<br>
IEEE Transactions on Signal Processing, <b>50</b>:174 - 188.<br></dd>
- <dt><a name="Breto2009">Bretó, C., D. He, E. L. Ionides, and A. A. King. 2009.</a></dt>
+ <dt><a name="Breto2009" id="Breto2009">Bretó, C., D. He, E. L. Ionides, and A. A. King. 2009.</a></dt>
<dd>Time series analysis via mechanistic models.<br>
Annals of Applied Statistics, <b>3</b>:319-348.<br></dd>
- <dt><a name="Ellner1998">Ellner, S. P., B. A. Bailey, G. V. Bobashev, A. R. Gallant, B. T. Grenfell, and D. W. Nychka. 1998.</a></dt>
+ <dt><a name="Ellner1998" id="Ellner1998">Ellner, S. P., B. A. Bailey, G. V. Bobashev, A. R. Gallant, B. T. Grenfell, and D. W. Nychka. 1998.</a></dt>
<dd>Noise and nonlinearity in measles epidemics: Combining mechanistic and statistical approaches to population modeling.<br>
American Naturalist, <b>151</b>:425-440.<br></dd>
- <dt><a name="He2010">He, D., E. L. Ionides, and A. A. King. 2010.</a></dt>
+ <dt><a name="He2010" id="He2010">He, D., E. L. Ionides, and A. A. King. 2010.</a></dt>
<dd>Plug-and-play inference for disease dynamics: measles in large and small populations as a case study.<br>
Journal of the Royal Society Interface, <b>7</b>:271-283.<br></dd>
- <dt><a name="Ionides2006">Ionides, E. L., C. Bretó, and A. A. King. 2006.</a></dt>
+ <dt><a name="Ionides2006" id="Ionides2006">Ionides, E. L., C. Bretó, and A. A. King. 2006.</a></dt>
<dd>Inference for nonlinear dynamical systems.<br>
Proceedings of the National Academy of Sciences of the U.S.A., <b>103</b>:18438-18443.<br></dd>
- <dt><a name="Kendall1999">Kendall, B. E., C. J. Briggs, W. W. Murdoch, P. Turchin, S. P. Ellner, E. McCauley, R. M. Nisbet, and S. N. Wood. 1999.</a></dt>
+ <dt><a name="Kendall1999" id="Kendall1999">Kendall, B. E., C. J. Briggs, W. W. Murdoch, P. Turchin, S. P. Ellner, E. McCauley, R. M. Nisbet, and S. N. Wood. 1999.</a></dt>
<dd>Why do populations cycle? A synthesis of statistical and mechanistic modeling approaches.<br>
Ecology, <b>80</b>:1789-1805.<br></dd>
- <dt><a name="Kendall2005">Kendall, B. E., S. P. Ellner, E. McCauley, S. N. Wood, C. J. Briggs, W. M. Murdoch, and P. Turchin. 2005.</a></dt>
+ <dt><a name="Kendall2005" id="Kendall2005">Kendall, B. E., S. P. Ellner, E. McCauley, S. N. Wood, C. J. Briggs, W. M. Murdoch, and P. Turchin. 2005.</a></dt>
<dd>Population cycles in the pine looper moth: Dynamical tests of mechanistic hypotheses.<br>
Ecological Monographs, <b>75</b>:259-276.<br></dd>
- <dt><a name="King2008">King, A. A., E. L. Ionides, M. Pascual, and M. J. Bouma. 2008.</a></dt>
+ <dt><a name="King2008" id="King2008">King, A. A., E. L. Ionides, M. Pascual, and M. J. Bouma. 2008.</a></dt>
<dd>Inapparent infections and cholera dynamics.<br>
Nature, <b>454</b>:877-880.<br></dd>
- <dt><a name="Liu2001b">Liu, J. and M. West. 2001.</a></dt>
+ <dt><a name="Liu2001b" id="Liu2001b">Liu, J. and M. West. 2001.</a></dt>
<dd>Combining Parameter and State Estimation in Simulation-Based Filtering.<br>
In A. Doucet, N. de Freitas, and N. J. Gordon, editors, Sequential Monte Carlo Methods in Practice, pages 197-224. Springer, New York.<br></dd>
- <dt><a name="Reuman2006">Reuman, D. C., R. A. Desharnais, R. F. Costantino, O. S. Ahmad, and J. E. Cohen. 2006.</a></dt>
+ <dt><a name="Reuman2006" id="Reuman2006">Reuman, D. C., R. A. Desharnais, R. F. Costantino, O. S. Ahmad, and J. E. Cohen. 2006.</a></dt>
<dd>Power spectra reveal the influence of stochasticity on nonlinear population dynamics.<br>
Proceedings of the National Academy of Sciences of the U.S.A., <b>103</b>:18860-18865.<br></dd>
- <dt><a name="Wood2010">Wood, S. N. 2010.</a></dt>
+ <dt><a name="Toni2010" id="Toni2010">Toni, T. and M. P. H. Stumpf. 2010.</a></dt>
+ <dd>Simulation-based model selection for dynamical systems in systems and population biology.<br>
+ Bioinformatics, <b>26</b>:104-110.<br></dd>
+
+ <dt><a name="Toni2009a" id="Toni2009a">Toni, T., D. Welch, N. Strelkowa, A. Ipsen, and M. P. H. Stumpf. 2009.</a></dt>
+
+ <dd>Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems.<br>
+ Journal of the Royal Society, Interface, <b>6</b>:187-202.<br></dd>
+
+ <dt><a name="Wood2010" id="Wood2010">Wood, S. N. 2010.</a></dt>
+
<dd>Statistical inference for noisy nonlinear ecological dynamic systems.<br>
Nature, <b>466</b>:1102-1104.<br></dd>
</dl>
Modified: www/content/refs.tex
===================================================================
--- www/content/refs.tex 2014-03-21 15:46:30 UTC (rev 903)
+++ www/content/refs.tex 2014-03-21 17:41:09 UTC (rev 904)
@@ -16,9 +16,11 @@
\nocite{King2008}
\nocite{Liu2001b}
\nocite{Reuman2006}
+\nocite{Toni2009a}
+\nocite{Toni2010}
\nocite{Wood2010}
\bibliographystyle{ecology}
-\bibliography{pomp}
+\bibliography{../vignettes/pomp}
\end{document}
Modified: www/vignettes/pomp.bib
===================================================================
--- www/vignettes/pomp.bib 2014-03-21 15:46:30 UTC (rev 903)
+++ www/vignettes/pomp.bib 2014-03-21 17:41:09 UTC (rev 904)
@@ -1,585 +1,384 @@
-% This file was created with JabRef 2.7b.
+% This file was created with JabRef 2.10.
% Encoding: UTF8
- at ARTICLE{Andrieu2010,
- author = {Andrieu, Christophe and Doucet, Arnaud and Holenstein, Roman},
- title = {{P}article {M}arkov chain {M}onte {C}arlo methods},
- journal = {Journal of the Royal Statistical Society, Series B},
- year = {2010},
- volume = {72},
- pages = {269--342},
- number = {3},
- abstract = {Markov chain Monte Carlo and sequential Monte Carlo methods have emerged
- as the two main tools to sample from high dimensional probability
- distributions. Although asymptotic convergence of Markov chain Monte
- Carlo algorithms is ensured under weak assumptions, the performance
- of these algorithms is unreliable when the proposal distributions
- that are used to explore the space are poorly chosen and/or if highly
- correlated variables are updated independently. We show here how
- it is possible to build efficient high dimensional proposal distributions
- by using sequential Monte Carlo methods. This allows us not only
- to improve over standard Markov chain Monte Carlo schemes but also
- to make Bayesian inference feasible for a large class of statistical
- models where this was not previously so. We demonstrate these algorithms
- on a non-linear state space model and a Levy-driven stochastic volatility
- model.},
- doi = {10.1111/j.1467-9868.2009.00736.x},
- file = {Andrieu2010.pdf:Andrieu2010.pdf:PDF},
- owner = {kingaa},
- timestamp = {2010.06.30}
+
+ at Article{Andrieu2010,
+ Title = {{P}article {M}arkov chain {M}onte {C}arlo methods},
+ Author = {Andrieu, Christophe and Doucet, Arnaud and Holenstein, Roman},
+ Journal = {Journal of the Royal Statistical Society, Series B},
+ Year = {2010},
+ Number = {3},
+ Pages = {269--342},
+ Volume = {72},
+
+ Abstract = {Markov chain Monte Carlo and sequential Monte Carlo methods have emerged as the two main tools to sample from high dimensional probability distributions. Although asymptotic convergence of Markov chain Monte Carlo algorithms is ensured under weak assumptions, the performance of these algorithms is unreliable when the proposal distributions that are used to explore the space are poorly chosen and/or if highly correlated variables are updated independently. We show here how it is possible to build efficient high dimensional proposal distributions by using sequential Monte Carlo methods. This allows us not only to improve over standard Markov chain Monte Carlo schemes but also to make Bayesian inference feasible for a large class of statistical models where this was not previously so. We demonstrate these algorithms on a non-linear state space model and a Levy-driven stochastic volatility model.},
+ Doi = {10.1111/j.1467-9868.2009.00736.x},
+ File = {Andrieu2010.pdf:Andrieu2010.pdf:PDF},
+ Owner = {kingaa},
+ Timestamp = {2010.06.30}
}
- at ARTICLE{Arulampalam2002,
- author = {Arulampalam, M. S. and Maskell, S. and Gordon, N. and Clapp, T.},
- title = {{A} {T}utorial on {P}article {F}ilters for {O}nline {N}onlinear,
- {N}on-{G}aussian {B}ayesian {T}racking},
- journal = {IEEE Transactions on Signal Processing},
- year = {2002},
- volume = {50},
- pages = {174 -- 188},
- doi = {10.1109/78.978374},
- file = {Arulampalam2002.pdf:Desktop/Arulampalam2002.pdf:PDF},
- owner = {kingaa},
- timestamp = {2007.07.20}
+ at Article{Arulampalam2002,
+ Title = {{A} {T}utorial on {P}article {F}ilters for {O}nline {N}onlinear, {N}on-{G}aussian {B}ayesian {T}racking},
+ Author = {Arulampalam, M. S. and Maskell, S. and Gordon, N. and Clapp, T.},
+ Journal = {IEEE Transactions on Signal Processing},
+ Year = {2002},
+ Pages = {174 -- 188},
+ Volume = {50},
+
+ Doi = {10.1109/78.978374},
+ File = {Arulampalam2002.pdf:Desktop/Arulampalam2002.pdf:PDF},
+ Owner = {kingaa},
+ Timestamp = {2007.07.20}
}
- at ARTICLE{Bhadra2010,
- author = {Bhadra, Anindya},
- title = {{D}iscussion of `{P}article {M}arkov chain {M}onte {C}arlo methods'
- by {C}.\ {A}ndrieu, {A}.\ {D}oucet and {R}.\ {H}olenstein},
- journal = {Journal of the Royal Statistical Society, Series B},
- year = {2010},
- volume = {72},
- pages = {314--315},
- doi = {10.1111/j.1467-9868.2009.00736.x},
- textref = {Bhadra, A. (2010), Discussion of `Particle Markov chain Monte Carlo
- methods' by C. Andrieu, A. Doucet and R. Holenstein, J. Roy. Stat.
- Soc B 72:314-315}
+ at Article{Bhadra2010,
+ Title = {{D}iscussion of `{P}article {M}arkov chain {M}onte {C}arlo methods' by {C}.\ {A}ndrieu, {A}.\ {D}oucet and {R}.\ {H}olenstein},
+ Author = {Bhadra, Anindya},
+ Journal = {Journal of the Royal Statistical Society, Series B},
+ Year = {2010},
+ Pages = {314--315},
+ Volume = {72},
+
+ Doi = {10.1111/j.1467-9868.2009.00736.x},
+ Textref = {Bhadra, A. (2010), Discussion of `Particle Markov chain Monte Carlo methods' by C. Andrieu, A. Doucet and R. Holenstein, J. Roy. Stat. Soc B 72:314-315}
}
- at ARTICLE{Breto2009,
- author = {Carles Bret\'{o} and Daihai He and Edward L. Ionides and Aaron A.
- King},
- title = {{T}ime series analysis via mechanistic models},
- journal = {Annals of Applied Statistics},
- year = {2009},
- volume = {3},
- pages = {319--348},
- number = {1},
- abstract = {The purpose of time series analysis via mechanistic models is to reconcile
- the known or hypothesized structure of a dynamical system with observations
- collected over time. We develop a framework for constructing nonlinear
- mechanistic models and carrying out inference. Our framework permits
- the consideration of implicit dynamic models, meaning statistical
- models for stochastic dynamical systems which are specified by a
- simulation algorithm to generate sample paths. Inference procedures
- that operate on implicit models are said to have the plug-and-play
- property. Our work builds on recently developed plug-and-play inference
- methodology for partially observed Markov models. We introduce a
- class of implicitly specified Markov chains with stochastic transition
- rates, and we demonstrate its applicability to open problems in statistical
- inference for biological systems. As one example, these models are
- shown to give a fresh perspective on measles transmission dynamics.
- As a second example, we present a mechanistic analysis of cholera
- incidence data, involving interaction between two competing strains
- of the pathogen Vibrio cholerae.},
- doi = {10.1214/08-AOAS201},
- file = {Breto2009.pdf:Breto2009.pdf:PDF;:Breto2009_supp.pdf:PDF},
- owner = {kingaa},
- timestamp = {2009.09.15}
+ at Article{Breto2011,
+ Title = {Compound Markov counting processes and their applications to modeling infinitesimally over-dispersed systems},
+ Author = {Bret{\'o}, Carles and Ionides, Edward L.},
+ Journal = {Stochastic Processes and their Applications},
+ Year = {2011},
+
+ Month = nov,
+ Number = {11},
+ Pages = {2571--2591},
+ Volume = {121},
+
+ __markedentry = {[kingaa:]},
+ Abstract = {We propose an infinitesimal dispersion index for Markov counting processes. We show that, under standard moment existence conditions, a process is infinitesimally (over-)equi-dispersed if, and only if, it is simple (compound), i.e. it increases in jumps of one (or more) unit(s), even though infinitesimally equi-dispersed processes might be under-, equi- or over-dispersed using previously studied indices. Compound processes arise, for example, when introducing continuous-time white noise to the rates of simple processes resulting in L{\'e}vy-driven SDEs. We construct multivariate infinitesimally over-dispersed compartment models and queuing networks, suitable for applications where moment constraints inherent to simple processes do not hold.},
+ Doi = {10.1016/j.spa.2011.07.005},
+ File = {Breto2011.pdf:Breto2011.pdf:PDF},
+ ISSN = {0304-4149},
+ Keywords = {Continuous time, Counting Markov process, Birth–death process, Environmental stochasticity, Infinitesimal over-dispersion, Simultaneous events},
+ Owner = {kingaa},
+ Timestamp = {2012.01.17}
}
- at ARTICLE{Breto2011,
- author = {Bret{\'o}, Carles and Ionides, Edward L.},
- title = {Compound Markov counting processes and their applications to modeling
- infinitesimally over-dispersed systems},
- journal = {Stochastic Processes and their Applications},
- year = {2011},
- volume = {121},
- pages = {2571--2591},
- number = {11},
- month = nov,
- __markedentry = {[kingaa:]},
- abstract = {We propose an infinitesimal dispersion index for Markov counting processes.
- We show that, under standard moment existence conditions, a process
- is infinitesimally (over-)equi-dispersed if, and only if, it is simple
- (compound), i.e. it increases in jumps of one (or more) unit(s),
- even though infinitesimally equi-dispersed processes might be under-,
- equi- or over-dispersed using previously studied indices. Compound
- processes arise, for example, when introducing continuous-time white
- noise to the rates of simple processes resulting in L{\'e}vy-driven
- SDEs. We construct multivariate infinitesimally over-dispersed compartment
- models and queuing networks, suitable for applications where moment
- constraints inherent to simple processes do not hold.},
- doi = {10.1016/j.spa.2011.07.005},
- file = {Breto2011.pdf:Breto2011.pdf:PDF},
- issn = {0304-4149},
- keywords = {Continuous time, Counting Markov process, Birth–death process, Environmental
- stochasticity, Infinitesimal over-dispersion, Simultaneous events},
- owner = {kingaa},
- timestamp = {2012.01.17}
+ at Article{Breto2009,
+ Title = {{T}ime series analysis via mechanistic models},
+ Author = {Carles Bret\'{o} and Daihai He and Edward L. Ionides and Aaron A. King},
+ Journal = {Annals of Applied Statistics},
+ Year = {2009},
+ Number = {1},
+ Pages = {319--348},
+ Volume = {3},
+
+ Abstract = {The purpose of time series analysis via mechanistic models is to reconcile the known or hypothesized structure of a dynamical system with observations collected over time. We develop a framework for constructing nonlinear mechanistic models and carrying out inference. Our framework permits the consideration of implicit dynamic models, meaning statistical models for stochastic dynamical systems which are specified by a simulation algorithm to generate sample paths. Inference procedures that operate on implicit models are said to have the plug-and-play property. Our work builds on recently developed plug-and-play inference methodology for partially observed Markov models. We introduce a class of implicitly specified Markov chains with stochastic transition rates, and we demonstrate its applicability to open problems in statistical inference for biological systems. As one example, these models are shown to give a fresh perspective on measles transmission dynamics. As a second example, we present a mechanistic analysis of cholera incidence data, involving interaction between two competing strains of the pathogen Vibrio cholerae.},
+ Doi = {10.1214/08-AOAS201},
+ File = {Breto2009.pdf:Breto2009.pdf:PDF;:Breto2009_supp.pdf:PDF},
+ Owner = {kingaa},
+ Timestamp = {2009.09.15}
}
- at BOOK{Davison1997,
- title = {{B}ootstrap {M}ethods and their {A}pplication},
- publisher = {Cambridge University Press},
- year = {1997},
- author = {A.C. Davison and D.V. Hinkley},
- address = {New York},
- owner = {kingaa},
- timestamp = {2011.05.26}
+ at Book{Davison1997,
+ Title = {{B}ootstrap {M}ethods and their {A}pplication},
+ Author = {A.C. Davison and D.V. Hinkley},
+ Publisher = {Cambridge University Press},
+ Year = {1997},
+
+ Address = {New York},
+
+ Owner = {kingaa},
+ Timestamp = {2011.05.26}
}
- at ARTICLE{Ellner1998,
- author = {S. P. Ellner and B. A. Bailey and G. V. Bobashev and A. R. Gallant
- and B. T. Grenfell and D. W. Nychka},
- title = {{N}oise and nonlinearity in measles epidemics: {C}ombining mechanistic
- and statistical approaches to population modeling},
- journal = {American Naturalist},
- year = {1998},
- volume = {151},
- pages = {425--440},
- abstract = {We present and evaluate an approach to analyzing population dynamics
- data using semimechanistic models. These models incorporate reliable
- information on population structure and underlying dynamic mechanisms
- but use nonparametric surface-fitting methods to avoid unsupported
- assumptions about the precise form of rate equations. Using historical
- data on measles epidemics as a case study, we show how this approach
- can lead to better forecasts, better characterizations of the dynamics,
- and a better understanding of the factors causing complex population
- dynamics relative to either mechanistic models or purely descriptive
- statistical time-series models. The semimechanistic models are found
- to have better forecasting accuracy than either of the model types
- used in previous analyses when tested on data not used to fit the
- models. The dynamics are characterized as being both nonlinear and
- noisy, and the global dynamics are clustered very tightly near the
- border of stability (dominant Lyapunov exponent lambda approximate
- to 0). However, locally in state space the dynamics oscillate between
- strong short-term stability and strong short-term chaos (i.e., between
- negative and positive local Lyapunov exponents). There is statistically
- significant evidence for short-term chaos in all data sets examined.
- Thus the nonlinearity in these systems is characterized by the variance
- over state space in local measures of chaos versus stability rather
- than a single summary measure of the overall dynamics as either chaotic
- or nonchaotic.},
- file = {Ellner1998.pdf:Ellner1998.pdf:PDF},
- owner = {kingaa},
- timestamp = {2009.09.22}
+ at Article{Ellner1998,
+ Title = {{N}oise and nonlinearity in measles epidemics: {C}ombining mechanistic and statistical approaches to population modeling},
+ Author = {S. P. Ellner and B. A. Bailey and G. V. Bobashev and A. R. Gallant and B. T. Grenfell and D. W. Nychka},
+ Journal = {American Naturalist},
+ Year = {1998},
+ Pages = {425--440},
+ Volume = {151},
+
+ Abstract = {We present and evaluate an approach to analyzing population dynamics data using semimechanistic models. These models incorporate reliable information on population structure and underlying dynamic mechanisms but use nonparametric surface-fitting methods to avoid unsupported assumptions about the precise form of rate equations. Using historical data on measles epidemics as a case study, we show how this approach can lead to better forecasts, better characterizations of the dynamics, and a better understanding of the factors causing complex population dynamics relative to either mechanistic models or purely descriptive statistical time-series models. The semimechanistic models are found to have better forecasting accuracy than either of the model types used in previous analyses when tested on data not used to fit the models. The dynamics are characterized as being both nonlinear and noisy, and the global dynamics are clustered very tightly near the border of stability (dominant Lyapunov exponent lambda approximate to 0). However, locally in state space the dynamics oscillate between strong short-term stability and strong short-term chaos (i.e., between negative and positive local Lyapunov exponents). There is statistically significant evidence for short-term chaos in all data sets examined. Thus the nonlinearity in these systems is characterized by the variance over state space in local measures of chaos versus stability rather than a single summary measure of the overall dynamics as either chaotic or nonchaotic.},
+ File = {Ellner1998.pdf:Ellner1998.pdf:PDF},
+ Owner = {kingaa},
+ Timestamp = {2009.09.22}
}
- at ARTICLE{Gillespie1977a,
- author = {D. T. Gillespie},
- title = {{E}xact {S}tochastic {S}imulation of {C}oupled {C}hemical {R}eactions},
- journal = {Journal of Physical Chemistry},
- year = {1977},
- volume = {81},
- pages = {2340--2361},
- file = {Gillespie1977a.pdf:Gillespie1977a.pdf:PDF},
- owner = {kingaa},
- timestamp = {2007.03.13}
+ at Article{Gillespie1977a,
+ Title = {{E}xact {S}tochastic {S}imulation of {C}oupled {C}hemical {R}eactions},
+ Author = {D. T. Gillespie},
+ Journal = {Journal of Physical Chemistry},
+ Year = {1977},
+ Pages = {2340--2361},
+ Volume = {81},
+
+ File = {Gillespie1977a.pdf:Gillespie1977a.pdf:PDF},
+ Owner = {kingaa},
+ Timestamp = {2007.03.13}
}
- at BOOK{Gourieroux1996,
- title = {{S}imulation-based {E}conometric {M}ethods},
- publisher = {Oxford University Press},
- year = {1996},
- author = {C. Gouri\'{e}roux and A. Monfort},
- owner = {kingaa},
- timestamp = {2011.05.26}
+ at Book{Gourieroux1996,
+ Title = {{S}imulation-based {E}conometric {M}ethods},
+ Author = {C. Gouri\'{e}roux and A. Monfort},
+ Publisher = {Oxford University Press},
+ Year = {1996},
+
+ Owner = {kingaa},
+ Timestamp = {2011.05.26}
}
- at ARTICLE{He2010,
- author = {He, Daihai and Ionides, Edward L. and King, Aaron A.},
- title = {{P}lug-and-play inference for disease dynamics: measles in large
- and small populations as a case study},
- journal = {Journal of the Royal Society Interface},
- year = {2010},
- volume = {7},
- pages = {271--283},
- month = jun,
- abstract = {Statistical inference for mechanistic models of partially observed
- dynamic systems is an active area of research. Most existing inference
- methods place substantial restrictions upon the form of models that
- can be fitted and hence upon the nature of the scientific hypotheses
- that can be entertained and the data that can be used to evaluate
- them. In contrast, the so-called methods require only simulations
- from a model and are thus free of such restrictions. We show the
- utility of the plug-and-play approach in the context of an investigation
- of measles transmission dynamics. Our novel methodology enables us
- to ask and answer questions that previous analyses have been unable
- to address. Specifically, we demonstrate that plug-and-play methods
- permit the development of a modelling and inference framework applicable
- to data from both large and small populations. We thereby obtain
- novel insights into the nature of heterogeneity in mixing and comment
- on the importance of including extra-demographic stochasticity as
- a means of dealing with environmental stochasticity and model misspecification.
- Our approach is readily applicable to many other epidemiological
- and ecological systems.},
- doi = {10.1098/rsif.2009.0151},
- file = {He2010.pdf:He2010.pdf:PDF;:He2010_Supplement.pdf:PDF},
- owner = {kingaa},
- timestamp = {2009.06.26}
+ at Article{He2010,
+ Title = {{P}lug-and-play inference for disease dynamics: measles in large and small populations as a case study},
+ Author = {He, Daihai and Ionides, Edward L. and King, Aaron A.},
+ Journal = {Journal of the Royal Society Interface},
+ Year = {2010},
+
+ Month = jun,
+ Pages = {271--283},
+ Volume = {7},
+
+ Abstract = {Statistical inference for mechanistic models of partially observed dynamic systems is an active area of research. Most existing inference methods place substantial restrictions upon the form of models that can be fitted and hence upon the nature of the scientific hypotheses that can be entertained and the data that can be used to evaluate them. In contrast, the so-called methods require only simulations from a model and are thus free of such restrictions. We show the utility of the plug-and-play approach in the context of an investigation of measles transmission dynamics. Our novel methodology enables us to ask and answer questions that previous analyses have been unable to address. Specifically, we demonstrate that plug-and-play methods permit the development of a modelling and inference framework applicable to data from both large and small populations. We thereby obtain novel insights into the nature of heterogeneity in mixing and comment on the importance of including extra-demographic stochasticity as a means of dealing with environmental stochasticity and model misspecification. Our approach is readily applicable to many other epidemiological and ecological systems.},
+ Doi = {10.1098/rsif.2009.0151},
+ File = {He2010.pdf:He2010.pdf:PDF;:He2010_Supplement.pdf:PDF},
+ Owner = {kingaa},
+ Timestamp = {2009.06.26}
}
- at ARTICLE{Ionides2011,
- author = {E. L. Ionides and A. Bhadra and Y. Atchad{\'e} and A. A. King},
- title = {Iterated filtering},
- journal = {Annals of Statistics},
- year = {2011},
- volume = {39},
- pages = {1776--1802},
- number = {3},
- abstract = {Inference for partially observed Markov process models has been a
- longstanding methodological challenge with many scientific and engineering
- applications. Iterated filtering algorithms maximize the likelihood
- function for partially observed Markov process models by solving
- a recursive sequence of filtering problems. We present new theoretical
- results pertaining to the convergence of iterated filtering algorithms
- implemented via sequential Monte Carlo filters. This theory complements
- the growing body of empirical evidence that iterated filtering algorithms
- provide an effective inference strategy for scientific models of
- nonlinear dynamic systems. The first step in our theory involves
- studying a new recursive approach for maximizing the likelihood function
- of a latent variable model, when this likelihood is evaluated via
- importance sampling. This leads to the consideration of an iterated
- importance sampling algorithm which serves as a simple special case
- of iterated filtering, and may have applicability in its own right.},
- doi = {10.1214/11-AOS886},
- file = {Ionides2011.pdf:Ionides2011.pdf:PDF},
- owner = {kingaa},
- timestamp = {2011.03.07}
+ at Article{Ionides2011,
+ Title = {Iterated filtering},
+ Author = {E. L. Ionides and A. Bhadra and Y. Atchad{\'e} and A. A. King},
+ Journal = {Annals of Statistics},
+ Year = {2011},
+ Number = {3},
+ Pages = {1776--1802},
+ Volume = {39},
+
[TRUNCATED]
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