22,023 research outputs found
Ecological non-linear state space model selection via adaptive particle Markov chain Monte Carlo (AdPMCMC)
We develop a novel advanced Particle Markov chain Monte Carlo algorithm that
is capable of sampling from the posterior distribution of non-linear state
space models for both the unobserved latent states and the unknown model
parameters. We apply this novel methodology to five population growth models,
including models with strong and weak Allee effects, and test if it can
efficiently sample from the complex likelihood surface that is often associated
with these models. Utilising real and also synthetically generated data sets we
examine the extent to which observation noise and process error may frustrate
efforts to choose between these models. Our novel algorithm involves an
Adaptive Metropolis proposal combined with an SIR Particle MCMC algorithm
(AdPMCMC). We show that the AdPMCMC algorithm samples complex, high-dimensional
spaces efficiently, and is therefore superior to standard Gibbs or Metropolis
Hastings algorithms that are known to converge very slowly when applied to the
non-linear state space ecological models considered in this paper.
Additionally, we show how the AdPMCMC algorithm can be used to recursively
estimate the Bayesian Cram\'er-Rao Lower Bound of Tichavsk\'y (1998). We derive
expressions for these Cram\'er-Rao Bounds and estimate them for the models
considered. Our results demonstrate a number of important features of common
population growth models, most notably their multi-modal posterior surfaces and
dependence between the static and dynamic parameters. We conclude by sampling
from the posterior distribution of each of the models, and use Bayes factors to
highlight how observation noise significantly diminishes our ability to select
among some of the models, particularly those that are designed to reproduce an
Allee effect
Metropolized Randomized Maximum Likelihood for sampling from multimodal distributions
This article describes a method for using optimization to derive efficient
independent transition functions for Markov chain Monte Carlo simulations. Our
interest is in sampling from a posterior density for problems in which
the dimension of the model space is large, is multimodal with regions
of low probability separating the modes, and evaluation of the likelihood is
expensive. We restrict our attention to the special case for which the target
density is the product of a multivariate Gaussian prior and a likelihood
function for which the errors in observations are additive and Gaussian
Stochastic Weighted Graphs: Flexible Model Specification and Simulation
In most domains of network analysis researchers consider networks that arise
in nature with weighted edges. Such networks are routinely dichotomized in the
interest of using available methods for statistical inference with networks.
The generalized exponential random graph model (GERGM) is a recently proposed
method used to simulate and model the edges of a weighted graph. The GERGM
specifies a joint distribution for an exponential family of graphs with
continuous-valued edge weights. However, current estimation algorithms for the
GERGM only allow inference on a restricted family of model specifications. To
address this issue, we develop a Metropolis--Hastings method that can be used
to estimate any GERGM specification, thereby significantly extending the family
of weighted graphs that can be modeled with the GERGM. We show that new
flexible model specifications are capable of avoiding likelihood degeneracy and
efficiently capturing network structure in applications where such models were
not previously available. We demonstrate the utility of this new class of
GERGMs through application to two real network data sets, and we further assess
the effectiveness of our proposed methodology by simulating non-degenerate
model specifications from the well-studied two-stars model. A working R version
of the GERGM code is available in the supplement and will be incorporated in
the gergm CRAN package.Comment: 33 pages, 6 figures. To appear in Social Network
Monte Carlo techniques for real-time quantum dynamics
The stochastic-gauge representation is a method of mapping the equation of
motion for the quantum mechanical density operator onto a set of equivalent
stochastic differential equations. One of the stochastic variables is termed
the "weight", and its magnitude is related to the importance of the stochastic
trajectory. We investigate the use of Monte Carlo algorithms to improve the
sampling of the weighted trajectories and thus reduce sampling error in a
simulation of quantum dynamics. The method can be applied to calculations in
real time, as well as imaginary time for which Monte Carlo algorithms are
more-commonly used. The method is applicable when the weight is guaranteed to
be real, and we demonstrate how to ensure this is the case. Examples are given
for the anharmonic oscillator, where large improvements over stochastic
sampling are observed.Comment: 28 pages, submitted to J. Comp. Phy
Phase transition between synchronous and asynchronous updating algorithms
We update a one-dimensional chain of Ising spins of length with
algorithms which are parameterized by the probability for a certain site to
get updated in one time step. The result of the update event itself is
determined by the energy change due to the local change in the configuration.
In this way we interpolate between the Metropolis algorithm at zero temperature
for of the order of 1/L and for large , and a synchronous deterministic
updating procedure for . As function of we observe a phase transition
between the stationary states to which the algorithm drives the system. These
are non-absorbing stationary states with antiferromagnetic domains for ,
and absorbing states with ferromagnetic domains for . This means
that above this transition the stationary states have lost any remnants to the
ferromagnetic Ising interaction. A measurement of the critical exponents shows
that this transition belongs to the universality class of parity conservation.Comment: 5 pages, 3 figure
Wang-Landau sampling in three-dimensional polymers
Monte Carlo simulations using Wang-Landau sampling are performed to study
three-dimensional chains of homopolymers on a lattice. We confirm the accuracy
of the method by calculating the thermodynamic properties of this system. Our
results are in good agreement with those obtained using Metropolis importance
sampling. This algorithm enables one to accurately simulate the usually hardly
accessible low-temperature regions since it determines the density of states in
a single simulation.Comment: 5 pages, 9 figures arch-ive/Brazilian Journal of Physic
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