A class of random hazard rates, that is defined as a mixture of an indicator kernel convoluted with a completely random measure, is of interest. We provide an explicit characterization of the posterior distribution of this mixture hazard rate model via a finite mixture of S-paths. A closed and tractable Bayes estimator for the hazard rate is derived to be a finite sum over S-paths. The path cha...

We address the problem of externally measuring aggregate functions over documents indexed by search engines, like corpus size, index freshness, and density of duplicates in the corpus. The recently proposed estimators for such quantities [5, 8] are biased due to inaccurate approximation of the so called “document degrees”. In addition, the estimators in [5] are quite costly, due to their relian...

Stochastic processes that involve the creation of objects and relations over time are widespread, but relatively poorly studied. For example, accurate fault diagnosis in factory assembly processes requires inferring the probabilities of erroneous assembly operations, but doing this efficiently and accurately is difficult. Modeled as dynamic Bayesian networks, these processes have discrete varia...

Inference on vertex-aligned graphs is of wide theoretical and practical importance. There are, however, few flexible tractable statistical models for correlated graphs, even fewer comprehensive approaches to parametric inference data arising from such graphs. In this paper, we consider the Bernoulli random graph model (allowing different coefficients edge correlations pairs vertices), introduce...

In urban areas or space-constrained environments with obstacles, vehicle localization using Global Navigation Satellite System (GNSS) data is hindered by Non-Line Of Sight (NLOS) and multipath receptions. These phenomena induce faulty data that disrupt the precise localization of the GNSS receiver. In this study, we detect the outliers among the observations, Pseudo-Range (PR) and/or Doppler me...

Prospecting for in situ mineral resources is essential for establishing settlements on the Moon and Mars. To reduce human effort and risk, it is desirable to build robotic systems to perform this prospecting. An important issue in designing such systems is the sampling strategy: how do the robots choose where to prospect next? This paper argues that a strategy called Adaptive Cluster Sampling (...

The intrinsic dimensionality of an inverse problem is affected by prior information, the accuracy and number of observations, and the smoothing properties of the forward operator. From a Bayesian perspective, changes from the prior to the posterior may, in many problems, be confined to a relatively lowdimensional subspace of the parameter space. We present a dimension reduction approach that de...

A class of random hazard rates, that is defined as a mixture of an indicator kernel convoluted with a completely random measure, is of interest. We provide an explicit characterization of the posterior distribution of this mixture hazard rate model via a finite mixture of S-paths. A closed and tractable Bayes estimator for the hazard rate is derived to be a finite sum over S-paths. The path cha...

Location based services are gathering an even wider interest also in indoor environments and urban canyons, where satellite systems like GPS are no longer accurate. A much addressed solution for estimating the user position exploits the received signal strengths (RSS) in wireless local area networks (WLANs), which are very common nowadays. However, the performances of RSS based location systems...

We study the problem of batch learning from bandit feedback in setting extremely large action spaces. Learning extreme is ubiquitous recommendation systems, which billions decisions are made over sets consisting millions choices a single day, yielding massive observational data. In these large-scale real-world applications, supervised frameworks such as eXtreme Multi-label Classification (XMC) ...