Search results for "Bayesian [statistics]"
showing 10 items of 228 documents
Application of a Bayesian Spatiotemporal Surveillance Method to NYC Syndromic Data
2014
Incorporating prior knowledge (e.g., the spatial distribution of zip codes and background population effects) into a model using Bayesian methods could potentially improve outbreak detection. We adapted a previously described Bayesian model-based spatiotemporal surveillance technique to daily respiratory syndrome counts in NYC Emergency Department data in 2009, the year of the H1N1 influenza pandemic. Citywide, 56 alarms were produced across 15 zip codes, all during days of elevated respiratory visits. Future work includes evaluating our choice of baseline length, considering other alarm thresholds, and conducting a formal evaluation of the method across five syndromes in NYC.
Toward a direct and scalable identification of reduced models for categorical processes.
2017
The applicability of many computational approaches is dwelling on the identification of reduced models defined on a small set of collective variables (colvars). A methodology for scalable probability-preserving identification of reduced models and colvars directly from the data is derived—not relying on the availability of the full relation matrices at any stage of the resulting algorithm, allowing for a robust quantification of reduced model uncertainty and allowing us to impose a priori available physical information. We show two applications of the methodology: (i) to obtain a reduced dynamical model for a polypeptide dynamics in water and (ii) to identify diagnostic rules from a standar…
What Bayesians Expect of Each Other
1991
Abstract Our goal is to study general properties of one Bayesian's subjective beliefs about the behavior of another Bayesian's subjective beliefs. We consider two Bayesians, A and B, who have different subjective distributions for a parameter θ, and study Bayesian A's expectation of Bayesian B's posterior distribution for θ given some data Y. We show that when θ can take only two values, Bayesian A always expects Bayesian B's posterior distribution to lie between the prior distributions of A and B. Conditions are given under which a similar result holds for an arbitrary real-valued parameter θ. For a vector parameter θ we present useful expressions for the mean vector and covariance matrix …
Bayesian Smoothing in the Estimation of the Pair Potential Function of Gibbs Point Processes
1999
A flexible Bayesian method is suggested for the pair potential estimation with a high-dimensional parameter space. The method is based on a Bayesian smoothing technique, commonly applied in statistical image analysis. For the calculation of the posterior mode estimator a new Monte Carlo algorithm is developed. The method is illustrated through examples with both real and simulated data, and its extension into truly nonparametric pair potential estimation is discussed.
On the Classification of Dynamical Data Streams Using Novel “Anti–Bayesian” Techniques
2018
The classification of dynamical data streams is among the most complex problems encountered in classification. This is, firstly, because the distribution of the data streams is non-stationary, and it changes without any prior “warning”. Secondly, the manner in which it changes is also unknown. Thirdly, and more interestingly, the model operates with the assumption that the correct classes of previously-classified patterns become available at a juncture after their appearance. This paper pioneers the use of unreported novel schemes that can classify such dynamical data streams by invoking the recently-introduced “Anti- Bayesian” (AB) techniques. Contrary to the Bayesian paradigm, that compar…
Properties of the Binary Neutron Star Merger GW170817
2019
On August 17, 2017, the Advanced LIGO and Advanced Virgo gravitational-wave detectors observed a low-mass compact binary inspiral. The initial sky localization of the source of the gravitational-wave signal, GW170817, allowed electromagnetic observatories to identify NGC 4993 as the host galaxy. In this work, we improve initial estimates of the binary's properties, including component masses, spins, and tidal parameters, using the known source location, improved modeling, and recalibrated Virgo data. We extend the range of gravitational-wave frequencies considered down to 23 Hz, compared to 30 Hz in the initial analysis. We also compare results inferred using several signal models, which ar…
Hydrological post-processing based on approximate Bayesian computation (ABC)
2019
[EN] This study introduces a method to quantify the conditional predictive uncertainty in hydrological post-processing contexts when it is cumbersome to calculate the likelihood (intractable likelihood). Sometimes, it can be difficult to calculate the likelihood itself in hydrological modelling, specially working with complex models or with ungauged catchments. Therefore, we propose the ABC post-processor that exchanges the requirement of calculating the likelihood function by the use of some sufficient summary statistics and synthetic datasets. The aim is to show that the conditional predictive distribution is qualitatively similar produced by the exact predictive (MCMC post-processor) or …
Epidemiology of Multiple Sclerosis en France
2012
In Europe, France is located between high and low risk areas of Multiple Sclerosis (MS). We estimated the national prevalence of MS in France on 31st October 2004 and the incidence between 2000 and 2007 based on data from the ‘Caisse Nationale d’Assurance Maladie des Travailleurs Salariés’ which insures 87% of the population. MS like other chronic diseases is one of the 30 long-term illnesses (Affections de Longue Durée, ALD). We analysed geographic variations in the prevalence and incidence of MS in France using the Bayesian approach.Total MS prevalence in France standardised for age was 94.7 per 100,000; 130.5 in women; 54.8 in men. The notification rate for MS (2000-2007) after age-stand…
Flexible Bayesian survival models with application in biometric studies
2018
El análisis de supervivencia es una metodología estadística diseñada para analizar datos procedentes de estudios científicos relativos a tiempos de ocurrencia de uno o varios eventos de interés. La duración de estos tiempos suele conocerse como tiempos de supervivencia debido a los particulares orígenes de esta metodología en contextos exclusivamente médicos y demográficos. Durante las últimas décadas, la literatura científica en este campo ha sido muy prolífica y su aplicación se ha extendido a múltiples áreas de conocimiento. Los procedimientos estadísticos propios de esta metodología empezaron a abordarse desde el marco inferencial frecuentista. Sin embargo, en los últimos años la utiliz…
Bayesian inference in Markovian queues
1994
This paper is concerned with the Bayesian analysis of general queues with Poisson input and exponential service times. Joint posterior distribution of the arrival rate and the individual service rate is obtained from a sample consisting inn observations of the interarrival process andm complete service times. Posterior distribution of traffic intensity inM/M/c is also obtained and the statistical analysis of the ergodic condition from a decision point of view is discussed.