6,635 research outputs found
Accelerating delayed-acceptance Markov chain Monte Carlo algorithms
Delayed-acceptance Markov chain Monte Carlo (DA-MCMC) samples from a
probability distribution via a two-stages version of the Metropolis-Hastings
algorithm, by combining the target distribution with a "surrogate" (i.e. an
approximate and computationally cheaper version) of said distribution. DA-MCMC
accelerates MCMC sampling in complex applications, while still targeting the
exact distribution. We design a computationally faster, albeit approximate,
DA-MCMC algorithm. We consider parameter inference in a Bayesian setting where
a surrogate likelihood function is introduced in the delayed-acceptance scheme.
When the evaluation of the likelihood function is computationally intensive,
our scheme produces a 2-4 times speed-up, compared to standard DA-MCMC.
However, the acceleration is highly problem dependent. Inference results for
the standard delayed-acceptance algorithm and our approximated version are
similar, indicating that our algorithm can return reliable Bayesian inference.
As a computationally intensive case study, we introduce a novel stochastic
differential equation model for protein folding data.Comment: 40 pages, 21 figures, 10 table
Efficient data augmentation for fitting stochastic epidemic models to prevalence data
Stochastic epidemic models describe the dynamics of an epidemic as a disease
spreads through a population. Typically, only a fraction of cases are observed
at a set of discrete times. The absence of complete information about the time
evolution of an epidemic gives rise to a complicated latent variable problem in
which the state space size of the epidemic grows large as the population size
increases. This makes analytically integrating over the missing data infeasible
for populations of even moderate size. We present a data augmentation Markov
chain Monte Carlo (MCMC) framework for Bayesian estimation of stochastic
epidemic model parameters, in which measurements are augmented with
subject-level disease histories. In our MCMC algorithm, we propose each new
subject-level path, conditional on the data, using a time-inhomogeneous
continuous-time Markov process with rates determined by the infection histories
of other individuals. The method is general, and may be applied, with minimal
modifications, to a broad class of stochastic epidemic models. We present our
algorithm in the context of multiple stochastic epidemic models in which the
data are binomially sampled prevalence counts, and apply our method to data
from an outbreak of influenza in a British boarding school
A bi-dimensional finite mixture model for longitudinal data subject to dropout
In longitudinal studies, subjects may be lost to follow-up, or miss some of
the planned visits, leading to incomplete response sequences. When the
probability of non-response, conditional on the available covariates and the
observed responses, still depends on unobserved outcomes, the dropout mechanism
is said to be non ignorable. A common objective is to build a reliable
association structure to account for dependence between the longitudinal and
the dropout processes. Starting from the existing literature, we introduce a
random coefficient based dropout model where the association between outcomes
is modeled through discrete latent effects. These effects are outcome-specific
and account for heterogeneity in the univariate profiles. Dependence between
profiles is introduced by using a bi-dimensional representation for the
corresponding distribution. In this way, we define a flexible latent class
structure which allows to efficiently describe both dependence within the two
margins of interest and dependence between them. By using this representation
we show that, unlike standard (unidimensional) finite mixture models, the non
ignorable dropout model properly nests its ignorable counterpart. We detail the
proposed modeling approach by analyzing data from a longitudinal study on the
dynamics of cognitive functioning in the elderly. Further, the effects of
assumptions about non ignorability of the dropout process on model parameter
estimates are (locally) investigated using the index of (local) sensitivity to
non-ignorability
Adoption of augmented reality technology by university students
In recent times, Augmented Reality has gained more relevance in the field of education. This relevance has been
enhanced due to its ease of use, as well as the availability of the technical devices for the students. The present
study was conducted with students enrolled in the Pedagogy Degree in the Faculty of Education at the University
of Seville. The objective was to understand the degree of technological acceptance of students during their
interaction with the AR objects produced, the performance achieved by the students, and if their gender affected
their acquisition of knowledge. For this, three data collection instruments were utilized: a multiple choice test for
the analysis of the student's performance after the interaction, the Technology Acceptance Model (TAM) diagnostic instrument, created by Davis (1989), and an “ad hoc” instrument created so that the students could
evaluate the class notes enriched with the AR objects created. The study has allowed us to broaden the scientific
knowledge of the TAM by Davis, to understand that AR objects can be utilized in university teaching, and to know
that the student's gender does not influence learning.Ministry of Economy and Competitiveness of Spain EDU-5746-
Hierarchical Models for Relational Event Sequences
Interaction within small groups can often be represented as a sequence of
events, where each event involves a sender and a recipient. Recent methods for
modeling network data in continuous time model the rate at which individuals
interact conditioned on the previous history of events as well as actor
covariates. We present a hierarchical extension for modeling multiple such
sequences, facilitating inferences about event-level dynamics and their
variation across sequences. The hierarchical approach allows one to share
information across sequences in a principled manner---we illustrate the
efficacy of such sharing through a set of prediction experiments. After
discussing methods for adequacy checking and model selection for this class of
models, the method is illustrated with an analysis of high school classroom
dynamics
Estimating the GARCH Diffusion: Simulated Maximum Likelihood in Continuous Time
A new algorithm is developed to provide a simulated maximum likelihood estimation of the GARCH diffusion model of Nelson (1990) based on return data only. The method combines two accurate approximation procedures, namely, the polynomial expansion of AĂŻt-Sahalia (2008) to approximate the transition probability density of return and volatility, and the Efficient Importance Sampler (EIS) of Richard and Zhang (2007) to integrate out the volatility. The first and second order terms in the polynomial expansion are used to generate a base-line importance density for an EIS algorithm. The higher order terms are included when evaluating the importance weights. Monte Carlo experiments show that the new method works well and the discretization error is well controlled by the polynomial expansion. In the empirical application, we fit the GARCH diffusion to equity data, perform diagnostics on the model fit, and test the finiteness of the importance weights.Ecient importance sampling; GARCH diusion model; Simulated Maximum likelihood; Stochastic volatility
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