Accelerated Parallel Non-conjugate Sampling for Bayesian Non-parametric Models

Inference of latent feature models in the Bayesian nonparametric setting is generally difficult, especially in high dimensional settings, because it usually requires proposing features from some prior distribution. In special cases, where the integration is tractable, we could sample new feature assignments according to a predictive likelihood. However, this still may not be efficient in high dimensions. We present a novel method to accelerate the mixing of latent variable model inference by proposing feature locations from the data, as opposed to the prior. First, we introduce our accelerated feature proposal mechanism that we will show is a valid Bayesian inference algorithm and next we propose an approximate inference strategy to perform accelerated inference in parallel. This sampling method is efficient for proper mixing of the Markov chain Monte Carlo sampler, computationally attractive, and is theoretically guaranteed to converge to the posterior distribution as its limiting distribution.Comment: Previously known as "Accelerated Inference for Latent Variable Models

Sequential Gaussian Processes for Online Learning of Nonstationary Functions

Many machine learning problems can be framed in the context of estimating functions, and often these are time-dependent functions that are estimated in real-time as observations arrive. Gaussian processes (GPs) are an attractive choice for modeling real-valued nonlinear functions due to their flexibility and uncertainty quantification. However, the typical GP regression model suffers from several drawbacks: i) Conventional GP inference scales $O(N^{3})$ with respect to the number of observations; ii) updating a GP model sequentially is not trivial; and iii) covariance kernels often enforce stationarity constraints on the function, while GPs with non-stationary covariance kernels are often intractable to use in practice. To overcome these issues, we propose an online sequential Monte Carlo algorithm to fit mixtures of GPs that capture non-stationary behavior while allowing for fast, distributed inference. By formulating hyperparameter optimization as a multi-armed bandit problem, we accelerate mixing for real time inference. Our approach empirically improves performance over state-of-the-art methods for online GP estimation in the context of prediction for simulated non-stationary data and hospital time series data

A Semi-Bayesian Nonparametric Estimator of the Maximum Mean Discrepancy Measure: Applications in Goodness-of-Fit Testing and Generative Adversarial Networks

A classic inferential statistical problem is the goodness-of-fit (GOF) test. Such a test can be challenging when the hypothesized parametric model has an intractable likelihood and its distributional form is not available. Bayesian methods for GOF can be appealing due to their ability to incorporate expert knowledge through prior distributions. However, standard Bayesian methods for this test often require strong distributional assumptions on the data and their relevant parameters. To address this issue, we propose a semi-Bayesian nonparametric (semi-BNP) procedure in the context of the maximum mean discrepancy (MMD) measure that can be applied to the GOF test. Our method introduces a novel Bayesian estimator for the MMD, enabling the development of a measure-based hypothesis test for intractable models. Through extensive experiments, we demonstrate that our proposed test outperforms frequentist MMD-based methods by achieving a lower false rejection and acceptance rate of the null hypothesis. Furthermore, we showcase the versatility of our approach by embedding the proposed estimator within a generative adversarial network (GAN) framework. It facilitates a robust BNP learning approach as another significant application of our method. With our BNP procedure, this new GAN approach can enhance sample diversity and improve inferential accuracy compared to traditional techniques.Comment: Typos corrected, Secondary (simulation and theoretical) results added, Additional discussion added, references adde

Probabilistic Time of Arrival Localization

In this letter, we take a new approach for time of arrival geo-localization. We show that the main sources of error in metropolitan areas are due to environmental imperfections that bias our solutions, and that we can rely on a probabilistic model to learn and compensate for them. The resulting localization error is validated using measurements from a live LTE cellular network to be less than 10 meters, representing an order-of-magnitude improvement

Nano-scale corrosion mechanism of T91 steel in static lead-bismuth eutectic: a combined APT, EBSD, and STEM investigation

T91 steel is a candidate material for structural components in lead-bismuth-eutectic (LBE) cooled systems, for example fast reactors and solar power plants [1]. However, the corrosion mechanisms of T91 in LBE remain poorly understood. In this study, we have analysed the static corrosion of T91 in liquid LBE using a range of characterisation techniques at increasingly smaller scales. A unique pattern of liquid metal intrusion was observed that does not appear to correlate with the grain boundary network. Upon closer inspection, electron backscatter diffraction (EBSD) reveals a change in the morphology of grains at the LBE-exposed surface, suggesting a local phase transition. Energy dispersive X-ray (EDX) maps show that Cr is depleted in the T91 material near the LBE interface. Furthermore, we observed the dissolution of all Cr-enriched precipitates in this region. Although the corrosion is conducted in an oxygen deficient environment, both scanning transmission electron microscopy (STEM) and atom probe tomography (APT) reveal a thin surface oxide layer (presumably wüstite) at the LBE-steel interface. Using electron energy loss spectroscopy (EELS) in the STEM, as well as APT, the atomic scale elemental redistribution and 3D morphology of the corrosion interface is investigated. By combining results from these different techniques, several types of oxide phases and structures can be identified. Based on this detailed nano-scale information, we propose potential mechanisms of T91 corrosion in LBE

The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

A promising alternative to comprehensively performing genomics experiments is to, instead, perform a subset of experiments and use computational methods to impute the remainder. However, identifying the best imputation methods and what measures meaningfully evaluate performance are open questions. We address these questions by comprehensively analyzing 23 methods from the ENCODE Imputation Challenge. We find that imputation evaluations are challenging and confounded by distributional shifts from differences in data collection and processing over time, the amount of available data, and redundancy among performance measures. Our analyses suggest simple steps for overcoming these issues and promising directions for more robust research

The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

A promising alternative to comprehensively performing genomics experiments is to, instead, perform a subset of experiments and use computational methods to impute the remainder. However, identifying the best imputation methods and what measures meaningfully evaluate performance are open questions. We address these questions by comprehensively analyzing 23 methods from the ENCODE Imputation Challenge. We find that imputation evaluations are challenging and confounded by distributional shifts from differences in data collection and processing over time, the amount of available data, and redundancy among performance measures. Our analyses suggest simple steps for overcoming these issues and promising directions for more robust research