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Bayesian Item Response Modelling in R with brms and StanMay 23 2019Item Response Theory (IRT) is widely applied in the human sciences to model persons' responses on a set of items measuring one or more latent constructs. While several R packages have been developed that implement IRT models, they tend to be restricted ... More

Nested sampling on non-trivial geometriesMay 22 2019Metropolis nested sampling evolves a Markov chain from a current livepoint and accepts new points along the chain according to a version of the Metropolis acceptance ratio modified to satisfy the likelihood constraint, characteristic of nested sampling ... More

Application of the interacting particle system method to piecewise deterministic Markov processes used in reliabilityMay 22 2019Variance reduction methods are often needed for the reliability assessment of complex industrial systems, we focus on one variance reduction method in a given context, that is the interacting particle system method (IPS) used on piecewise deterministic ... More

A Kalman particle filter for online parameter estimation with applications to affine modelsMay 21 2019In this paper we address the problem of estimating the posterior distribution of the static parameters of a continuous time state space model with discrete time observations by an algorithm that combines the Kalman filter and a particle filter. The proposed ... More

Efficient Profile Maximum Likelihood for Universal Symmetric Property EstimationMay 21 2019Estimating symmetric properties of a distribution, e.g. support size, coverage, entropy, distance to uniformity, are among the most fundamental problems in algorithmic statistics. While each of these properties have been studied extensively and separate ... More

Gaussian Process Learning via Fisher Scoring of Vecchia's ApproximationMay 20 2019We derive a single pass algorithm for computing the gradient and Fisher information of Vecchia's Gaussian process loglikelihood approximation, which provides a computationally efficient means for applying the Fisher scoring algorithm for maximizing the ... More

Tools for analyzing R code the tidy wayMay 20 2019With the current emphasis on reproducibility and replicability, there is an increasing need to examine how data analyses are conducted. In order to analyze the between researcher variability in data analysis choices as well as the aspects within the data ... More

Leveraging Bayesian Analysis To Improve Accuracy of Approximate ModelsMay 20 2019We focus on improving the accuracy of an approximate model of a multiscale dynamical system that uses a set of parameter-dependent terms to account for the effects of unresolved or neglected dynamics on resolved scales. We start by considering various ... More

Stratified sampling and resampling for approximate Bayesian computationMay 20 2019Approximate Bayesian computation (ABC) is computationally intensive for complex model simulators. To exploit expensive simulations, data-resampling was used with success in Everitt [2017] to obtain many artificial datasets at little cost and construct ... More

Estimating variances in time series linear regression models using empirical BLUPs and convex optimizationMay 19 2019We propose a two-stage estimation method of variance components in time series models known as FDSLRMs, whose observations can be described by a linear mixed model (LMM). We based estimating variances, fundamental quantities in a time series forecasting ... More

On greedy heuristics for computing D-efficient saturated subsetsMay 18 2019Let $\mathcal{F}$ be a set consisting of $n$ real vectors of dimension $m \leq n$. For any saturated, i.e., $m$-element, subset $\mathcal{S}$ of $\mathcal{F}$, let $\mathrm{vol}(\mathcal{S})$ be the volume of the parallelotope formed by the vectors of ... More

LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data ApproximationsMay 17 2019Due to the ease of modern data collection, applied statisticians often have access to a large set of covariates that they wish to relate to some observed outcome. Generalized linear models (GLMs) offer a particularly interpretable framework for such an ... More

Optimizing Interim Analysis Timing for Bayesian Adaptive Commensurate DesignsMay 17 2019In developing products for rare diseases, statistical challenges arise due to the limited number of patients available for participation in drug trials and other clinical research. Bayesian adaptive clinical trial designs offer the possibility of increased ... More

A Fast and Scalable Implementation Method for Competing Risks Data with the R Package fastcmprskMay 17 2019Advancements in medical informatics tools and high-throughput biological experimentation make large-scale biomedical data routinely accessible to researchers. Competing risks data are typical in biomedical studies where individuals are at risk to more ... More

Non-negative matrix factorization based on generalized dual divergenceMay 16 2019A theoretical framework for non-negative matrix factorization based on generalized dual Kullback-Leibler divergence, which includes members of the exponential family of models, is proposed. A family of algorithms is developed using this framework and ... More

Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent VariablesMay 16 2019The bits-back argument suggests that latent variable models can be turned into lossless compression schemes. Translating the bits-back argument into efficient and practical lossless compression schemes for general latent variable models, however, is still ... More

Yang-Baxter random fields and stochastic vertex modelsMay 16 2019Bijectivization refines the Yang-Baxter equation into a pair of local Markov moves which randomly update the configuration of the vertex model. Employing this approach, we introduce new Yang-Baxter random fields of Young diagrams based on spin $q$-Whittaker ... More

Finding our Way in the Dark: Approximate MCMC for Approximate Bayesian MethodsMay 16 2019With larger data at their disposal, scientists are emboldened to tackle complex questions that require sophisticated statistical models. It is not unusual for the latter to have likelihood functions that elude analytical formulations. Even under such ... More

4D Seismic History Matching Incorporating Unsupervised LearningMay 16 2019The work discussed and presented in this paper focuses on the history matching of reservoirs by integrating 4D seismic data into the inversion process using machine learning techniques. A new integrated scheme for the reconstruction of petrophysical properties ... More

The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High DimensionsMay 16 2019Discovering interaction effects on a response of interest is a fundamental problem faced in biology, medicine, economics, and many other scientific disciplines. In theory, Bayesian methods for discovering pairwise interactions enjoy many benefits such ... More

Iterative Alpha Expansion for estimating gradient-sparse signals from linear measurementsMay 15 2019We consider estimating a piecewise-constant image, or a gradient-sparse signal on a general graph, from noisy linear measurements. We propose and study an iterative algorithm to minimize a penalized least-squares objective, with a penalty given by the ... More

A New Estimation Algorithm for Box-Cox Transformation Cure Rate Model and Comparison With EM AlgorithmMay 15 2019In this paper, we develop a new estimation procedure based on the non-linear conjugate gradient (NCG) algorithm for the Box-Cox transformation cure rate model. We compare the performance of the NCG algorithm with the well-known expectation maximization ... More

Fractional Exclusion Statistics as an Occupancy ProcessMay 15 2019We show the possibility of describing fractional exclusion statistics (FES) as an occupancy process with global and \textit{local} exclusion constraints. More specifically, using combinatorial identities, we show that FES can be viewed as "ball-in-box" ... More

Approximate Bayesian computation via the energy statisticMay 14 2019Approximate Bayesian computation (ABC) has become an essential part of the Bayesian toolbox for addressing problems in which the likelihood is prohibitively expensive or entirely unknown, making it intractable. ABC defines a quasi-posterior by comparing ... More

Estimating Bayes factors from minimal ANOVA summaries for repeated-measures designsMay 14 2019In this paper, we develop a formula for estimating Bayes factors from repeated measures ANOVA designs. The formula, which requires knowing only minimal information about the ANOVA (e.g., the F -statistic), is based on the BIC approximation of the Bayes ... More

Convolutional Poisson Gamma Belief NetworkMay 14 2019For text analysis, one often resorts to a lossy representation that either completely ignores word order or embeds each word as a low-dimensional dense feature vector. In this paper, we propose convolutional Poisson factor analysis (CPFA) that directly ... More

Scaling Bayesian Probabilistic Record Linkage with Post-Hoc Blocking: An Application to the California Great RegistersMay 14 2019Probabilistic record linkage (PRL) is the process of determining which records in two databases correspond to the same underlying entity in the absence of a unique identifier. Bayesian solutions to this problem provide a powerful mechanism for propagating ... More

Variational approximations using Fisher divergenceMay 13 2019Modern applications of Bayesian inference involve models that are sufficiently complex that the corresponding posterior distributions are intractable and must be approximated. The most common approximation is based on Markov chain Monte Carlo, but these ... More

Replica Conditional Sequential Monte CarloMay 13 2019We propose a Markov chain Monte Carlo (MCMC) scheme to perform state inference in non-linear non-Gaussian state-space models. Current state-of-the-art methods to address this problem rely on particle MCMC techniques and its variants, such as the iterated ... More

The compound product distribution; a solution to the distributional equation X=AX+1May 12 2019The solution of $ X=AX+1 $ is analyzed for a discrete variable $ A $ with $ \mathbb{P}\left[A=0\right]>0 $. Accordingly, a fast algorithm is presented to calculate the obtained heavy tail density. To exemplify, the compound product distribution is studied ... More

Massive parallelization boosts big Bayesian multidimensional scalingMay 11 2019Big Bayes is the computationally intensive co-application of big data and large, expressive Bayesian models for the analysis of complex phenomena in scientific inference and statistical learning. Standing as an example, Bayesian multidimensional scaling ... More

Structural Equation Modeling using Computation GraphsMay 11 2019Structural equation modeling (SEM) is evolving as available data is becoming more complex, reaching the limits of what traditional estimation approaches can achieve. As SEM expands to ever larger, more complex applications, the estimation challenge grows ... More

Exploration of Gibbs-Laguerre tessellations for three-dimensional stochastic modelingMay 10 2019Random tessellations are well suited for the probabilistic modeling of three-dimensional (3D) grain microstructure of polycrystalline metals. The present paper deals with so-called Gibbs-Laguerre tessellations where the generators of a Laguerre tessellation ... More

Generating Random Samples from Non-Identical Truncated Order StatisticsMay 10 2019We provide an efficient algorithm to generate random samples from the bounded kth order statistic in a sample of independent, but not necessarily identically distributed, random variables. The bounds can be upper or lower bounds and need only hold on ... More

On the Efficacy of Monte Carlo Implementation of CAVIMay 09 2019In Variational Inference (VI), coordinate-ascent and gradient-based approaches are two major types of algorithms for approximating difficult-to-compute probability densities. In real-world implementations of complex models, Monte Carlo methods are widely ... More

Stein Point Markov Chain Monte CarloMay 09 2019An important task in machine learning and statistics is the approximation of a probability measure by an empirical measure supported on a discrete point set. Stein Points are a class of algorithms for this task, which proceed by sequentially minimising ... More

Fast online 3D reconstruction of dynamic scenes from individual single-photon detection eventsMay 08 2019In this paper, we present an algorithm for online 3D reconstruction of dynamic scenes using individual times of arrival (ToA) of photons recorded by single-photon detector arrays. One of the main challenges in 3D imaging using single-photon Lidar is the ... More

Non-Reversible Parallel Tempering: an Embarassingly Parallel MCMC SchemeMay 08 2019Parallel tempering (PT) methods are a popular class of Markov chain Monte Carlo schemes used to explore complex high-dimensional probability distributions. These algorithms can be highly effective but their performance is contingent on the selection of ... More

Multifidelity probability estimation via fusion of estimatorsMay 07 2019This paper develops a multifidelity method that enables estimation of failure probabilities for expensive-to-evaluate models via information fusion and importance sampling. The presented general fusion method combines multiple probability estimators with ... More

Somatic mutations render human exome and pathogen DNA more similarMay 07 2019Immunotherapy has recently shown important clinical successes in a substantial number of oncology indications. Additionally, the tumor somatic mutation load has been shown to associate with response to these therapeutic agents, and specific mutational ... More

Estimating the inverse trace using random forests on graphsMay 06 2019Some data analysis problems require the computation of (regularised) inverse traces, i.e. quantities of the form $\Tr (q \bI + \bL)^{-1}$. For large matrices, direct methods are unfeasible and one must resort to approximations, for example using a conjugate ... More

Computing a Data DividendMay 06 2019Quality data is a fundamental contributor to success in statistics and machine learning. If a statistical assessment or machine learning leads to decisions that create value, data contributors may want a share of that value. This paper presents methods ... More

Vertex Nomination, Consistent Estimation, and Adversarial ModificationMay 06 2019Given a pair of graphs $G_1$ and $G_2$ and a vertex set of interest in $G_1$, the vertex nomination problem seeks to find the corresponding vertices of interest in $G_2$ (if they exist) and produce a rank list of the vertices in $G_2$, with the corresponding ... More

Vertex Nomination, Consistent Estimation, and Adversarial ModificationMay 06 2019May 15 2019Given a pair of graphs $G_1$ and $G_2$ and a vertex set of interest in $G_1$, the vertex nomination problem seeks to find the corresponding vertices of interest in $G_2$ (if they exist) and produce a rank list of the vertices in $G_2$, with the corresponding ... More

Faster algorithms for polytope rounding, sampling, and volume computation via a sublinear "Ball Walk''May 05 2019We study the problem of "isotropically rounding" a polytope $K\subseteq\mathbb{R}^n$, that is, computing a linear transformation which makes the uniform distribution on the polytope have roughly identity covariance matrix. We assume that $K$ is defined ... More

A Bayesian Variational Framework for Stochastic OptimizationMay 05 2019This work proposes a theoretical framework for stochastic optimization algorithms, based on a continuous Bayesian variational model for algorithms. Using techniques from stochastic control with asymmetric information, the solution to this variational ... More

A Bayesian Variational Framework for Stochastic OptimizationMay 05 2019May 08 2019This work proposes a theoretical framework for stochastic optimization algorithms, based on a continuous-time Bayesian variational model. Using techniques from stochastic control with asymmetric information, the solution to this variational problem is ... More

A Bayesian Variational Framework for Stochastic OptimizationMay 05 2019May 07 2019This work proposes a theoretical framework for stochastic optimization algorithms, based on a continuous-time Bayesian variational model. Using techniques from stochastic control with asymmetric information, the solution to this variational problem is ... More

A Latent Variational Framework for Stochastic OptimizationMay 05 2019May 23 2019This paper provides a unifying theoretical framework for stochastic optimization algorithms by means of a latent stochastic variational problem. Using techniques from stochastic control, the solution to the variational problem is shown to be equivalent ... More

Regularized estimation for highly multivariate log Gaussian Cox processesMay 04 2019Statistical inference for highly multivariate point pattern data is challenging due to complex models with large numbers of parameters. In this paper, we develop numerically stable and efficient parameter estimation and model selection algorithms for ... More

ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical VariablesMay 04 2019To address the challenge of backpropagating the gradient through categorical variables, we propose the augment-REINFORCE-swap-merge (ARSM) gradient estimator that is unbiased and has low variance. ARSM first uses variable augmentation, REINFORCE, and ... More

Parallel Gaussian process surrogate method to accelerate likelihood-free inferenceMay 03 2019We consider Bayesian inference when only a limited number of noisy log-likelihood evaluations can be obtained. This occurs for example when complex simulator-based statistical models are fitted to data, and synthetic likelihood (SL) is used to form the ... More

Remote measurement of sea ice dynamics with regularized optimal transportMay 02 2019As Arctic conditions rapidly change, human activity in the Arctic will continue to increase and so will the need for high-resolution observations of sea ice. While satellite imagery can provide high spatial resolution, it is temporally sparse and significant ... More

Maximizing simulated tropical cyclone intensity with action minimizationMay 01 2019Direct computer simulation of intense tropical cyclones (TCs) in weather models is limited by computational expense. Intense TCs are rare and have small-scale structures, making it difficult to produce large ensembles of storms at high resolution. Further, ... More

Pushing the Limit: A Hybrid Parallel Implementation of the Multi-resolution Approximation for Massive DataApr 30 2019The multi-resolution approximation (MRA) of Gaussian processes was recently proposed to conduct likelihood-based inference for massive spatial data sets. An advantage of the methodology is that it can be parallelized. We implemented the MRA in C++ for ... More

Pushing the Limit: A Hybrid Parallel Implementation of the Multi-resolution Approximation for Massive DataApr 30 2019May 06 2019The multi-resolution approximation (MRA) of Gaussian processes was recently proposed to conduct likelihood-based inference for massive spatial data sets. An advantage of the methodology is that it can be parallelized. We implemented the MRA in C++ for ... More

On the parameter estimation of ARMA(p,q) model by approximate Bayesian computationApr 30 2019In this paper, the parameter estimation of ARMA(p,q) model is given by approximate Bayesian computation algorithm. In order to improve the sampling efficiency of the algorithm, approximate Bayesian computation should select as many statistics as possible ... More

A solution to the initial condition problems of inflation : NATONApr 29 2019The recent astonishing realization of the negative absolute temperature (NAT) for motional degrees of freedom \cite{Braun_ea13} inspires its possible application to the early universe. The existence of the upper bound on the energy of the system is the ... More

Multilevel adaptive sparse Leja approximations for Bayesian inverse problemsApr 27 2019Deterministic interpolation and quadrature methods are often unsuitable to address Bayesian inverse problems depending on computationally expensive forward mathematical models. While interpolation may give precise posterior approximations, deterministic ... More

Multilevel adaptive sparse Leja approximations for Bayesian inverse problemsApr 27 2019May 07 2019Deterministic interpolation and quadrature methods are often unsuitable to address Bayesian inverse problems depending on computationally expensive forward mathematical models. While interpolation may give precise posterior approximations, deterministic ... More

Optimal Scaling of Metropolis Algorithms on General Target DistributionsApr 27 2019The main limitation of the existing optimal scaling results for Metropolis--Hastings algorithms is that the assumptions on the target distribution are unrealistic. In this paper, we consider optimal scaling of random-walk Metropolis algorithms on general ... More

An R Package for Spatio-Temporal Change of SupportApr 27 2019Spatio-temporal change of support (STCOS) methods are designed for statistical inference and prediction on spatial and/or temporal domains which differ from the domains on which the data were observed. Bradley, Wikle, and Holan (2015; Stat) introduced ... More

Exponential Family Estimation via Adversarial Dynamics EmbeddingApr 27 2019We present an efficient algorithm for maximum likelihood estimation (MLE) of the general exponential family, even in cases when the energy function is represented by a deep neural network. We consider the primal-dual view of the MLE for the kinectics ... More

Smoothing and Interpolating Noisy GPS Data with Smoothing SplinesApr 26 2019A comprehensive methodology is provided for smoothing noisy, irregularly sampled data with non-Gaussian noise using smoothing splines. We demonstrate how the spline order and tension parameter can be chosen \emph{a priori} from physical reasoning. We ... More

New visualizations for Monte Carlo simulationsApr 26 2019In Monte Carlo simulations, samples are obtained from a target distribution in order to estimate various features. We present a flexible class of visualizations for assessing the quality of estimation, which are principled, practical, and easy to implement. ... More

Preferential attachment without vertex growth: emergence of the giant componentApr 26 2019We study the following preferential attachment variant of the classical Erdos-Renyi random graph process. Starting with an empty graph on n vertices, new edges are added one-by-one, and each time an edge is chosen with probability roughly proportional ... More

Evaluating the boundary and Stieltjes transform of limiting spectral distributions for random matrices with a separable variance profileApr 26 2019We present numerical algorithms for solving two problems encountered in random matrix theory and its applications. First, we compute the boundary of the limiting spectral distribution for random matrices with a separable variance profile. Second, we evaluate ... More

Sensitivity analysis based dimension reduction of multiscale modelsApr 25 2019In this paper, the sensitivity analysis of a single scale model is employed in order to reduce the input dimensionality of the related multiscale model, in this way, improving the efficiency of its uncertainty estimation. The approach is demonstrated ... More

Baseline Drift Estimation for Air Quality Data Using Quantile Trend FilteringApr 24 2019We address the problem of estimating smoothly varying baseline trends in time series data. This problem arises in a wide range of fields, including chemistry, macroeconomics, and medicine; however, our study is motivated by the analysis of data from low ... More

Comparing Samples from the $\mathcal{G}^0$ Distribution using a Geodesic DistanceApr 23 2019The $\mathcal{G}^0$ distribution is widely used for monopolarized SAR image modeling because it can characterize regions with different degree of texture accurately. It is indexed by three parameters: the number of looks (which can be estimated for the ... More

ssMousetrack: Analysing computerized tracking data via Bayesian state-space models in {R}Apr 23 2019Recent technological advances have provided new settings to enhance individual-based data collection and computerized-tracking data have became common in many behavioral and social research. By adopting instantaneous tracking devices such as computer-mouse, ... More

Unbiased Multilevel Monte Carlo: Stochastic Optimization, Steady-state Simulation, Quantiles, and Other ApplicationsApr 22 2019We present general principles for the design and analysis of unbiased Monte Carlo estimators in a wide range of settings. Our estimators posses finite work-normalized variance under mild regularity conditions. We apply our estimators to various settings ... More

Convergence of diffusions and their discretizations: from continuous to discrete processes and backApr 22 2019In this paper, we establish new quantitative convergence bounds for a class of functional autoregressive models in weighted total variation metrics. To derive this result, we show that under mild assumptions explicit minorization and Foster-Lyapunov drift ... More

A Maximum Entropy Procedure to Solve Likelihood EquationsApr 22 2019May 20 2019In this article we provide initial findings regarding the problem of solving likelihood equations by means of a maximum entropy approach. Unlike standard procedures that require equating at zero the score function of the maximum-likelihood problem, we ... More

A Maximum Entropy Procedure to Solve Likelihood EquationsApr 22 2019In this article we provide initial findings regarding the problem of solving likelihood equations by means of a maximum entropy approach. Unlike standard procedures that require equating at zero the score function of the maximum-likelihood problem, we ... More

Is infinity that far? A Bayesian nonparametric perspective of finite mixture modelsApr 22 2019Mixture models are one of the most widely used statistical tools when dealing with data from heterogeneous populations. This paper considers the long-standing debate over finite mixture and infinite mixtures and brings the two modelling strategies together, ... More

Kriging in Tensor Train data formatApr 21 2019Combination of low-tensor rank techniques and the Fast Fourier transform (FFT) based methods had turned out to be prominent in accelerating various statistical operations such as Kriging, computing conditional covariance, geostatistical optimal design, ... More

Particle filter efficiency under limited communicationApr 21 2019Sequential Monte Carlo (SMC) methods are typically not straightforward to implement on parallel architectures. This is because standard resampling schemes involve communication between all particles in the system. In this article, we consider the $\alpha$-SMC ... More

Conditionally structured variational Gaussian approximation with importance weightsApr 21 2019We develop flexible methods of deriving variational inference for models with complex latent variable structure. By splitting the variables in these models into "global" parameters and "local" latent variables, we define a class of variational approximations ... More

Continuous-Time Birth-Death MCMC for Bayesian Regression Tree ModelsApr 19 2019Decision trees are flexible models that are well suited for many statistical regression problems. In a Bayesian framework for regression trees, Markov Chain Monte Carlo (MCMC) search algorithms are required to generate samples of tree models according ... More

Modelling antimicrobial prescriptions in Scotland: A spatio-temporal clustering approachApr 18 2019In 2016 the British government acknowledged the importance of reducing antimicrobial prescriptions in order to avoid the long-term harmful effects of over-prescription. Prescription needs are highly dependent on factors that have a spatio-temporal component, ... More

Scalable Bayesian Inference for Population Markov Jump ProcessesApr 17 2019Bayesian inference for Markov jump processes (MJPs) where available observations relate to either system states or jumps typically relies on data-augmentation Markov Chain Monte Carlo. State-of-the-art developments involve representing MJP paths with ... More

High-dimensional variable selection via low-dimensional adaptive learningApr 17 2019A stochastic search method, the so-called Adaptive Subspace (AdaSub) method, is proposed for variable selection in high-dimensional linear regression models. The method aims at finding the best model with respect to a certain model selection criterion ... More

Forecasting with time series imagingApr 17 2019Feature-based time series representation has attracted substantial attention in a wide range of time series analysis methods. Recently, the use of time series features for forecast model selection and model averaging has been an emerging research focus ... More

High-dimensional copula variational approximation through transformationApr 16 2019Variational methods are attractive for computing Bayesian inference for highly parametrized models and large datasets where exact inference is impractical. They approximate a target distribution - either the posterior or an augmented posterior - using ... More

Global Error Bounds and Linear Convergence for Gradient-Based Algorithms for Trend Filtering and $\ell_{1}$-Convex ClusteringApr 16 2019We propose a class of first-order gradient-type optimization algorithms to solve structured \textit{filtering-clustering problems}, a class of problems which include trend filtering and $\ell_1$-convex clustering as special cases. Our first main result ... More

Fuel Economy Gaps Within & Across Garages: A Bivariate Random Parameters Seemingly Unrelated Regression ApproachApr 15 2019The key objective of this study is to investigate the interrelationship between fuel economy gaps and to quantify the differential effects of several factors on fuel economy gaps of vehicles operated by the same garage. By using a unique fuel economy ... More

Approximate Bayesian Inference via Sparse grid Quadrature Evaluation for Hierarchical ModelsApr 15 2019We combine conditioning techniques with sparse grid quadrature rules to develop a computationally efficient method to approximate marginal, but not necessarily univariate, posterior quantities, yielding approximate Bayesian inference via Sparse grid Quadrature ... More

Applications of Quantum Annealing in StatisticsApr 15 2019Quantum computation offers exciting new possibilities for statistics. This paper explores the use of the D-Wave machine, a specialized type of quantum computer, which performs quantum annealing. A general description of quantum annealing through the use ... More

Community Detection in the Sparse Hypergraph Stochastic Block ModelApr 11 2019We consider the community detection problem in sparse random hypergraphs. Angelini et al. (2015) conjectured the existence of a sharp threshold on model parameters for community detection in sparse hypergraphs generated by a hypergraph stochastic block ... More

High dimensional optimal design using stochastic gradient optimisation and Fisher information gainApr 11 2019Finding high dimensional designs is increasingly important in applications of experimental design, but is computationally demanding under existing methods. We introduce an efficient approach applying recent advances in stochastic gradient optimisation ... More

Markov chain Monte Carlo importance samplers for Bayesian models with intractable likelihoodsApr 11 2019We consider the efficient use of an approximation within Markov chain Monte Carlo (MCMC), with subsequent importance sampling (IS) correction of the Markov chain inexact output, leading to asymptotically exact inference. We detail convergence and central ... More

Joint state-parameter estimation of a nonlinear stochastic energy balance model from sparse noisy dataApr 10 2019While nonlinear stochastic partial differential equations arise naturally in spatiotemporal modeling, inference for such systems often faces two major challenges: sparse noisy data and ill-posedness of the inverse problem of parameter estimation. To overcome ... More

Gradient-Free Multi-Agent Nonconvex Nonsmooth OptimizationApr 09 2019In this paper, we consider the problem of minimizing the sum of nonconvex and possibly nonsmooth functions over a connected multi-agent network, where the agents have partial knowledge about the global cost function and can only access the zeroth-order ... More

Robust Approximate Bayesian Inference with Synthetic LikelihoodApr 09 2019Bayesian synthetic likelihood (BSL) is now a well-established method for conducting approximate Bayesian inference in complex models where exact Bayesian approaches are either infeasible, or computationally demanding, due to the intractability of likelihood ... More

A sensitivity analysis of the PAWN sensitivity indexApr 09 2019The PAWN index is gaining traction among the modelling community as a moment-independent method to conduct global sensitivity analysis. However, it has been used so far without knowing how robust it is to its main design parameters, which need to be defined ... More

CRAD: Clustering with Robust Autocuts and DepthApr 08 2019We develop a new density-based clustering algorithm named CRAD which is based on a new neighbor searching function with a robust data depth as the dissimilarity measure. Our experiments prove that the new CRAD is highly competitive at detecting clusters ... More

A Generalization Bound for Online Variational InferenceApr 08 2019Bayesian inference provides an attractive online-learning framework to analyze sequential data, and offers generalization guarantees which hold even under model mismatch and with adversaries. Unfortunately, exact Bayesian inference is rarely feasible ... More

A Fast Scheme for the Uniform Sampling of Binary Matrices with Fixed MarginsApr 08 2019Uniform sampling of binary matrix with fixed margins is an important and difficult problem in statistics, computer science, ecology and so on. The well-known swap algorithm would be inefficient when the size of the matrix becomes large or when the matrix ... More

From Co-prime to the Diophantine Equation Based Sparse Sensing in Complex WaveformsApr 07 2019For frequency estimation, the co-prime sampling tells that in time domain, by two sub-Nyquist samplers with M and N down sampling rates, respectively, up to O(MN) frequencies can be estimated based on autocorrelation, where M and N are co-prime. Similarly, ... More