Search results for "Bayesian [statistical analysis]"
showing 10 items of 299 documents
Physics-aware Gaussian processes in remote sensing
2018
Abstract Earth observation from satellite sensory data poses challenging problems, where machine learning is currently a key player. In recent years, Gaussian Process (GP) regression has excelled in biophysical parameter estimation tasks from airborne and satellite observations. GP regression is based on solid Bayesian statistics, and generally yields efficient and accurate parameter estimates. However, GPs are typically used for inverse modeling based on concurrent observations and in situ measurements only. Very often a forward model encoding the well-understood physical relations between the state vector and the radiance observations is available though and could be useful to improve pre…
Aerial Spectrum Surveying: Radio Map Estimation with Autonomous UAVs
2020
Radio maps are emerging as a popular means to endow next-generation wireless communications with situational awareness. In particular, radio maps are expected to play a central role in unmanned aerial vehicle (UAV) communications since they can be used to determine interference or channel gain at a spatial location where a UAV has not been before. Existing methods for radio map estimation utilize measurements collected by sensors whose locations cannot be controlled. In contrast, this paper proposes a scheme in which a UAV collects measurements along a trajectory. This trajectory is designed to obtain accurate estimates of the target radio map in a short time operation. The route planning a…
Particle Group Metropolis Methods for Tracking the Leaf Area Index
2020
Monte Carlo (MC) algorithms are widely used for Bayesian inference in statistics, signal processing, and machine learning. In this work, we introduce an Markov Chain Monte Carlo (MCMC) technique driven by a particle filter. The resulting scheme is a generalization of the so-called Particle Metropolis-Hastings (PMH) method, where a suitable Markov chain of sets of weighted samples is generated. We also introduce a marginal version for the goal of jointly inferring dynamic and static variables. The proposed algorithms outperform the corresponding standard PMH schemes, as shown by numerical experiments.
Surface soil water content estimation based on thermal inertia and Bayesian smoothing
2014
Soil water content plays a critical role in agro-hydrology since it regulates the rainfall partition between surface runoff and infiltration and, the energy partition between sensible and latent heat fluxes. Current thermal inertia models characterize the spatial and temporal variability of water content by assuming a sinusoidal behavior of the land surface temperature between subsequent acquisitions. Such behavior implicitly supposes clear sky during the whole interval between the thermal acquisitions; but, since this assumption is not necessarily verified even if sky is clear at the exact epoch of acquisition, , the accuracy of the model may be questioned due to spatial and temporal varia…
What Does Objective Mean in a Dirichlet-multinomial Process?
2017
Summary The Dirichlet-multinomial process can be seen as the generalisation of the binomial model with beta prior distribution when the number of categories is larger than two. In such a scenario, setting informative prior distributions when the number of categories is great becomes difficult, so the need for an objective approach arises. However, what does objective mean in the Dirichlet-multinomial process? To deal with this question, we study the sensitivity of the posterior distribution to the choice of an objective Dirichlet prior from those presented in the available literature. We illustrate the impact of the selection of the prior distribution in several scenarios and discuss the mo…
The geography of Spanish bank branches
2014
This article analyzes the determinants of bank branch location in Spain taking the role of geography explicitly into account. After a long period of intense territorial expansion, especially by savings banks, many of these firms are now involved in merger processes triggered off by the financial crisis, most of which entail the closing of many branches. However, given the contributions of this type of banks to limit financial exclusion, this process might exacerbate the consequences of the crisis for some disadvantaged social groups. Related problems such as new banking regulation initiatives (Basel III), or the current excess capacity in the sector add further relevance to this problem. We…
Asymptotic optimality of myopic information-based strategies for Bayesian adaptive estimation
2016
This paper presents a general asymptotic theory of sequential Bayesian estimation giving results for the strongest, almost sure convergence. We show that under certain smoothness conditions on the probability model, the greedy information gain maximization algorithm for adaptive Bayesian estimation is asymptotically optimal in the sense that the determinant of the posterior covariance in a certain neighborhood of the true parameter value is asymptotically minimal. Using this result, we also obtain an asymptotic expression for the posterior entropy based on a novel definition of almost sure convergence on "most trials" (meaning that the convergence holds on a fraction of trials that converge…
Correcting for non-ignorable missingness in smoking trends
2015
Data missing not at random (MNAR) is a major challenge in survey sampling. We propose an approach based on registry data to deal with non-ignorable missingness in health examination surveys. The approach relies on follow-up data available from administrative registers several years after the survey. For illustration we use data on smoking prevalence in Finnish National FINRISK study conducted in 1972-1997. The data consist of measured survey information including missingness indicators, register-based background information and register-based time-to-disease survival data. The parameters of missingness mechanism are estimable with these data although the original survey data are MNAR. The u…
A Bayesian analysis of classical hypothesis testing
1980
The procedure of maximizing the missing information is applied to derive reference posterior probabilities for null hypotheses. The results shed further light on Lindley’s paradox and suggest that a Bayesian interpretation of classical hypothesis testing is possible by providing a one-to-one approximate relationship between significance levels and posterior probabilities.
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 …