550 research outputs found
Exploiting the feller coupling for the ewens sampling formula
This is the final version of the article. It first appeared from the Institute of Mathematical Statistics via http://dx.doi.org/10.1214/15-STS53
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The linear birthdeath process: An inferential retrospective
Abstract
In this paper we provide an introduction to statistical inference for the classical linear birth‒death process, focusing on computational aspects of the problem in the setting of discretely observed processes. The basic probabilistic properties are given in Section 2, focusing on computation of the transition functions. This is followed by a brief discussion of simulation methods in Section 3, and of frequentist methods in Section 4. Section 5 is devoted to Bayesian methods, from rejection sampling to Markov chain Monte Carlo and approximate Bayesian computation. In Section 6 we consider the time-inhomogeneous case. The paper ends with a brief discussion in Section 7.
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Why are acute admissions to hospital of children under 5 years of age increasing in the UK?
Children’s use of hospital services in the UK has been increasing rapidly since the late 1990s.1–6 Findings from the latest QualityWatch report show significant increases in emergency hospital admissions for infants (23%) and young children aged 1–4 years (11%) between 2006/2007 and 2015/2016 (data have been adjusted for population increases in each childhood age group), while children over the age of 15 years showed a decrease in emergency admissions
Effect of selection on ancestry: an exactly soluble case and its phenomenological generalization
We consider a family of models describing the evolution under selection of a
population whose dynamics can be related to the propagation of noisy traveling
waves. For one particular model, that we shall call the exponential model, the
properties of the traveling wave front can be calculated exactly, as well as
the statistics of the genealogy of the population. One striking result is that,
for this particular model, the genealogical trees have the same statistics as
the trees of replicas in the Parisi mean-field theory of spin glasses. We also
find that in the exponential model, the coalescence times along these trees
grow like the logarithm of the population size. A phenomenological picture of
the propagation of wave fronts that we introduced in a previous work, as well
as our numerical data, suggest that these statistics remain valid for a larger
class of models, while the coalescence times grow like the cube of the
logarithm of the population size.Comment: 26 page
The Time Machine: A Simulation Approach for Stochastic Trees
In the following paper we consider a simulation technique for stochastic
trees. One of the most important areas in computational genetics is the
calculation and subsequent maximization of the likelihood function associated
to such models. This typically consists of using importance sampling (IS) and
sequential Monte Carlo (SMC) techniques. The approach proceeds by simulating
the tree, backward in time from observed data, to a most recent common ancestor
(MRCA). However, in many cases, the computational time and variance of
estimators are often too high to make standard approaches useful. In this paper
we propose to stop the simulation, subsequently yielding biased estimates of
the likelihood surface. The bias is investigated from a theoretical point of
view. Results from simulation studies are also given to investigate the balance
between loss of accuracy, saving in computing time and variance reduction.Comment: 22 Pages, 5 Figure
Parameter uncertainty of a dynamic multispecies size spectrum model
Dynamic size spectrum models have been recognized as an effective way of describing how size-based interactions can give rise to the size structure of aquatic communities. They are intermediate-complexity ecological models that are solutions to partial differential equations driven by the size-dependent processes of predation, growth, mortality, and reproduction in a community of interacting species and sizes. To be useful for quantitative fisheries management these models need to be developed further in a formal statistical framework. Previous work has used time-averaged data to “calibrate” the model using optimization methods with the disadvantage of losing detailed time-series information. Using a published multispecies size spectrum model parameterized for the North Sea comprising 12 interacting fish species and a background resource, we fit the model to time-series data using a Bayesian framework for the first time. We capture the 1967–2010 period using annual estimates of fishing mortality rates as input to the model and time series of fisheries landings data to fit the model to output. We estimate 38 key parameters representing the carrying capacity of each species and background resource, as well as initial inputs of the dynamical system and errors on the model output. We then forecast the model forward to evaluate how uncertainty propagates through to population- and community-level indicators under alternative management strategies
Noisy traveling waves: effect of selection on genealogies
For a family of models of evolving population under selection, which can be
described by noisy traveling wave equations, the coalescence times along the
genealogical tree scale like , where is the size of the
population, in contrast with neutral models for which they scale like . An
argument relating this time scale to the diffusion constant of the noisy
traveling wave leads to a prediction for which agrees with our
simulations. An exactly soluble case gives trees with statistics identical to
those predicted for mean-field spin glasses in Parisi's theory.Comment: 4 pages, 2 figures New version includes more numerical simulations
and some rewriting of the text presenting our result
Evolution of the most recent common ancestor of a population with no selection
We consider the evolution of a population of fixed size with no selection.
The number of generations to reach the first common ancestor evolves in
time. This evolution can be described by a simple Markov process which allows
one to calculate several characteristics of the time dependence of . We also
study how is correlated to the genetic diversity.Comment: 21 pages, 10 figures, uses RevTex4 and feynmf.sty Corrections :
introduction and conclusion rewritten, references adde
Non-linear regression models for Approximate Bayesian Computation
Approximate Bayesian inference on the basis of summary statistics is
well-suited to complex problems for which the likelihood is either
mathematically or computationally intractable. However the methods that use
rejection suffer from the curse of dimensionality when the number of summary
statistics is increased. Here we propose a machine-learning approach to the
estimation of the posterior density by introducing two innovations. The new
method fits a nonlinear conditional heteroscedastic regression of the parameter
on the summary statistics, and then adaptively improves estimation using
importance sampling. The new algorithm is compared to the state-of-the-art
approximate Bayesian methods, and achieves considerable reduction of the
computational burden in two examples of inference in statistical genetics and
in a queueing model.Comment: 4 figures; version 3 minor changes; to appear in Statistics and
Computin
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