Search results for "Bayesian Inference"

Graph Topology Learning and Signal Recovery Via Bayesian Inference

2019

The estimation of a meaningful affinity graph has become a crucial task for representation of data, since the underlying structure is not readily available in many applications. In this paper, a topology inference framework, called Bayesian Topology Learning, is proposed to estimate the underlying graph topology from a given set of noisy measurements of signals. It is assumed that the graph signals are generated from Gaussian Markov Random Field processes. First, using a factor analysis model, the noisy measured data is represented in a latent space and its posterior probability density function is found. Thereafter, by utilizing the minimum mean square error estimator and the Expectation M…

Bayesian Model Averaging and Weighted Average Least Squares: Equivariance, Stability, and Numerical Issues

2011

This article is concerned with the estimation of linear regression models with uncertainty about the choice of the explanatory variables. We introduce the Stata commands bma and wals which implement, respectively, the exact Bayesian Model Averaging (BMA) estimator and the Weighted Average Least Squares (WALS) estimator developed by Magnus et al. (2010). Unlike standard pretest estimators which are based on some preliminary diagnostic test, these model averaging estimators provide a coherent way of making inference on the regression parameters of interest by taking into account the uncertainty due to both the estimation and the model selection steps. Special emphasis is given to a number pra…

Forecasting correlated time series with exponential smoothing models

2011

Abstract This paper presents the Bayesian analysis of a general multivariate exponential smoothing model that allows us to forecast time series jointly, subject to correlated random disturbances. The general multivariate model, which can be formulated as a seemingly unrelated regression model, includes the previously studied homogeneous multivariate Holt-Winters’ model as a special case when all of the univariate series share a common structure. MCMC simulation techniques are required in order to approach the non-analytically tractable posterior distribution of the model parameters. The predictive distribution is then estimated using Monte Carlo integration. A Bayesian model selection crite…

BEM-Based Magnetic Field Reconstruction by Ensemble Kálmán Filtering

2022

Abstract Magnetic fields generated by normal or superconducting electromagnets are used to guide and focus particle beams in storage rings, synchrotron light sources, mass spectrometers, and beamlines for radiotherapy. The accurate determination of the magnetic field by measurement is critical for the prediction of the particle beam trajectory and hence the design of the accelerator complex. In this context, state-of-the-art numerical field computation makes use of boundary-element methods (BEM) to express the magnetic field. This enables the accurate computation of higher-order partial derivatives and local expansions of magnetic potentials used in efficient numerical codes for particle tr…

A Bayesian Learning Automaton for Solving Two-Armed Bernoulli Bandit Problems

2008

The two-armed Bernoulli bandit (TABB) problem is a classical optimization problem where an agent sequentially pulls one of two arms attached to a gambling machine, with each pull resulting either in a reward or a penalty. The reward probabilities of each arm are unknown, and thus one must balance between exploiting existing knowledge about the arms, and obtaining new information. In the last decades, several computationally efficient algorithms for tackling this problem have emerged, with learning automata (LA) being known for their ?-optimality, and confidence interval based for logarithmically growing regret. Applications include treatment selection in clinical trials, route selection in …

Book Review: Another Science Is Possible

2018

The Wage Curve, Once More with Feeling: Bayesian Model Averaging of Heckit Models

2018

The sensitivity of the wage curve to sample-selection and model uncertainty was evaluated with Bayesian methods. More than 8000 Heckit wage curves were estimated using data from the 2017 household survey of Bolivia. After averaging the estimates with the posterior probability of each model being true, the wage curve elasticity in Bolivia is close to -0.01. This result suggests that in this country the wage curve is inelastic and does not follow the international statistical regularity of wage curves. 

Bayesian applications in dynamic econometric models

2009

The purpose of this thesis is to provide a few new ideas to the field of Bayesian econometrics. In particular, the focus of the thesis is on analyzing dynamic econometric models. In the first essay, we provide an easily implementable method for the Bayesian analysis of a simple hybrid DSGE model of Clarida et al. (1999). The forecasting properties of the model are tested against commonly used forecasting tools, such as Bayesian VARs and naïve forecasts based on univariate random walks. In particular, the predictability of three key macroeconomic-variables, inflation, short-term nominal interest rate and a measure of output gap, are studied using quarterly ex post and real-time U.S. data.Our…

Inference and prediction in bulk arrival queues and queues with service in stages

1998

This paper deals with the statistical analysis from a Bayesian point of view, of bulk arrival queues where the batch size is considered as a fixed constant. The focus is on prediction of the usual measures of performance of the system in the steady state. The probability generating function of the posterior predictive distribution of the number of customers in the system and the Laplace transform of the posterior predictive distribution of the waiting time in the system are obtained. Numerical inversion of these transforms is considered. Inference and prediction of its equivalent single queue with service in stages is also discussed.

Bayesian Modelling of Confusability of Phoneme-Grapheme Connections

2007

Deficiencies in the ability to map letters to sounds are currently considered to be the most likely early signs of dyslexia. This has motivated the use of Literate, a computer game for training this skill, in several Finnish schools and households as a tool in the early prevention of reading disability. In this paper, we present a Bayesian model that uses a student's performance in a game like Literate to infer which phoneme-grapheme connections student currently confuses with each other. This information can be used to adapt the game to a particular student's skills as well as to provide information about the student's learning progress to their parents and teachers. We apply our model to …