130 research outputs found
Approximate Bayesian Computational methods
Also known as likelihood-free methods, approximate Bayesian computational
(ABC) methods have appeared in the past ten years as the most satisfactory
approach to untractable likelihood problems, first in genetics then in a
broader spectrum of applications. However, these methods suffer to some degree
from calibration difficulties that make them rather volatile in their
implementation and thus render them suspicious to the users of more traditional
Monte Carlo methods. In this survey, we study the various improvements and
extensions made to the original ABC algorithm over the recent years.Comment: 7 figure
Bayesian Imputation of Revolving Doors
Political scientists and sociologists study how individuals switch back and
forth between public and private organizations, for example between regulator
and lobbyist positions, a phenomenon called "revolving doors". However, they
face an important issue of data missingness, as not all data relevant to this
question is freely available. For example, the nomination of an individual in a
given public-sector position of power might be publically disclosed, but not
their subsequent positions in the private sector. In this article, we adopt a
Bayesian data augmentation strategy for discrete time series and propose
measures of public-private mobility across the French state at large,
mobilizing administrative and digital data. We relax homogeneity hypotheses of
traditional hidden Markov models and implement a version of a Markov switching
model, which allows for varying parameters across individuals and time and
auto-correlated behaviors. We describe how the revolving doors phenomenon
varies across the French state and how it has evolved between 1990 and 2022.Comment: 29 pages, 6 figures and 6 table
On Particle Learning
This document is the aggregation of six discussions of Lopes et al. (2010)
that we submitted to the proceedings of the Ninth Valencia Meeting, held in
Benidorm, Spain, on June 3-8, 2010, in conjunction with Hedibert Lopes' talk at
this meeting, and of a further discussion of the rejoinder by Lopes et al.
(2010). The main point in those discussions is the potential for degeneracy in
the particle learning methodology, related with the exponential forgetting of
the past simulations. We illustrate in particular the resulting difficulties in
the case of mixtures.Comment: 14 pages, 9 figures, discussions on the invited paper of Lopes,
Carvalho, Johannes, and Polson, for the Ninth Valencia International Meeting
on Bayesian Statistics, held in Benidorm, Spain, on June 3-8, 2010. To appear
in Bayesian Statistics 9, Oxford University Press (except for the final
discussion
TraitLab: a Matlab package for fitting and simulating binary tree-like data
TraitLab is a software package for simulating, fitting and analysing
tree-like binary data under a stochastic Dollo model of evolution. The model
also allows for rate heterogeneity through catastrophes, evolutionary events
where many traits are simultaneously lost while new ones arise, and borrowing,
whereby traits transfer laterally between species as well as through ancestral
relationships. The core of the package is a Markov chain Monte Carlo (MCMC)
sampling algorithm that enables the user to sample from the Bayesian joint
posterior distribution for tree topologies, clade and root ages, and the trait
loss, catastrophe and borrowing rates for a given data set. Data can be
simulated according to the fitted Dollo model or according to a number of
generalized models that allow for heterogeneity in the trait loss rate, biases
in the data collection process and borrowing of traits between lineages.
Coupled pairs of Markov chains can be used to diagnose MCMC mixing and
convergence and to debias MCMC estimators. The raw data, MCMC run output, and
model fit can be inspected using a number of useful graphical and analytical
tools provided within the package or imported into other popular analysis
programs. TraitLab is freely available and runs within the Matlab computing
environment with its Statistics and Machine Learning toolbox, no other
additional toolboxes are required.Comment: Manual describing the TraitLab software for phylogenetic inferenc
Semaine d'Etude Mathématiques et Entreprises 1 : Modèles de comparaison quantitative de matrices 3D
Air Liquide dispose de jeux de données représentant des champs de quantité de dépôt de particules sur les poumons. Ces données proviennent de mesures 3D (obtenues via SPECT, Single Photon Emission Computer Tomography) du dépôt d'un aérosol contenant des particules radio-labellisées préalablement inhalé par les patients étudiés. Le sujet proposé ici consiste à se demander quelles sont les méthodes permettant de comparer de façon quantitative et systématique les résultats des observations, qui sont donnés sous la forme de matrices 3D correspondant à la quantité de particules estimée dans chaque voxel
Component-wise approximate Bayesian computation via Gibbs-like step
Approximate Bayesian computation methods are useful for generative models with intractable likelihoods. These methods are however sensitive to the dimension of the parameter space, requiring exponentially increasing resources as this dimension grows. To tackle this difficulty, we explore a Gibbs version of the Approximate Bayesian computation approach that runs component-wise approximate Bayesian computation steps aimed at the corresponding conditional posterior distributions, and based on summary statistics of reduced dimensions. While lacking the standard justifications for the Gibbs sampler, the resulting Markov chain is shown to converge in distribution under some partial independence conditions.The associated stationary distribution can further be shown to be close to the true posterior distribution and some hierarchical versions of the proposed mechanism enjoy a closed form limiting distribution. Experiments also demonstrate the gain in efficiency brought by the Gibbs version over the standard solution
Automated bolus advisor control and usability study (ABACUS): does use of an insulin bolus advisor improve glycaemic control in patients failing multiple daily insulin injection (MDI) therapy? [NCT01460446]
BACKGROUND: People with T1DM and insulin-treated T2DM often do not follow and/or adjust their insulin regimens as needed. Key contributors to treatment non-adherence are fear of hypoglycaemia, difficulty and lack of self-efficacy associated with insulin dose determination. Because manual calculation of insulin boluses is both complex and time consuming, people may rely on empirical estimates, which can result in persistent hypoglycaemia and/or hyperglycaemia. Use of automated bolus advisors (BA) has been shown to help insulin pump users to more accurately meet prandial insulin dosage requirements, improve postprandial glycaemic excursions, and achieve optimal glycaemic control with an increased time within optimal range. Use of a BA containing an early algorithm based on sliding scales for insulin dosing has also been shown to improve HbA1c levels in people treated with multiple daily insulin injections (MDI). We designed a study to determine if use of an automated BA can improve clinical and psychosocial outcomes in people treated with MDI. METHODS/DESIGN: The Automated Bolus Advisor Control and Usability Study (ABACUS) is a 6-month, prospective, randomised, multi-centre, multi-national trial to determine if automated BA use improves glycaemic control as measured by a change in HbA1c in people using MDI with elevated HbA1c levels (#62;7.5%). A total of 226 T1DM and T2DM participants will be recruited. Anticipated attrition of 20% will yield a sample size of 90 participants, which will provide #62;80% power to detect a mean difference of 0.5%, with SD of 0.9%, using a one-sided 5% t-test, with 5% significance level. Other measures of glycaemic control, self-care behaviours and psychosocial issues will also be assessed. DISCUSSION: It is critical that healthcare providers utilise available technologies that both facilitate effective glucose management and address concerns about safety and lifestyle. Automated BAs may help people using MDI to manage their diabetes more effectively and minimise the risk of long-term diabetes related complications. Findings from a recent study suggest that BA use positively addresses both safety and lifestyle concerns; however, randomised trials are needed to confirm these perceptions and determine whether bolus advisor use improves clinical outcomes. Our study is designed to make these assessments. TRIAL REGISTRATION: NCT0146044
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