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n-gram profiles have been successfully and widely used to analyse long sequences of potentially differing lengths for clustering or classification. Mainly, machine learning algorithms have been used for this purpose but, despite their predictive ...

• We develop a feature allocation model for grouped data with binary attributes and demonstrate its use on n-gram data.
• Show how the model can be estimated using a simple, exact Markov chain Monte Carlo method.
• Introduce a post-hoc ...

The process of training feedforward neural networks (FFNNs) can benefit from an automated process where the best heuristic to train the network is sought out automatically by means of a high-level probabilistic-based heuristic. This research ...

• A novel Bayesian hyper-heuristic (BHH) is developed and shown to efficiently train feedforward neural networks (FFNNs).
• The BHH shows statistically significant performance on multiple problems, comparable to the best heuristics.
• ...

The family of multivariate unified skew-normal (SUN) distributions has been recently shown to possess fundamental conjugacy properties. When used as priors for the vector of coefficients in probit, tobit, and multinomial probit models, these ...

Given a target probability density known up to a normalizing constant, the Metropolis–Hastings algorithm simulates realizations from a Markov chain which are eventual realizations from the target probability density. A key element for ensuring a ...

State‐of‐the‐art methods for convergence analysis of Metropolis‐Hastings algorithms are considered and reviewed. Practically important topics are discussed for an interdisciplinary audience. This includes convergence properties in high dimensions, proper ...

This work researches the drivers of e-commerce conversion of one of the largest e-commerce companies in the Netherlands. We focus on product page conversion, i.e., the probability that a customer who visits a specific product page also buys the ...

• We explain the drivers behind e-commerce conversion.
• We transform Big Data into valuable insights using Bayesian statistics.
• We use the consensus MCMC algorithm.

Generalized linear models (GLMs) form one of the most popular classes of models in statistics. The gamma variant is used, for instance, in actuarial science for the modelling of claim amounts in insurance. A flaw of GLMs is that they are not ...

Recent research is revealing how cognitive processes are supported by a complex interplay between the brain and the rest of the body, which can be investigated by the analysis of physiological features such as breathing rhythms, heart rate, and ...

• Heart rate dynamics is conceptualised as a hidden stochastic process that drive the observed heartbeats.
• State-space and Bayesian modelling are used to derive estimates of non-linear HR properties.
• These estimates can exhibiting a ...

Joint models for longitudinal and time-to-event data simultaneously model longitudinal and time-to-event information to avoid bias by combining usually a linear mixed model with a proportional hazards model. This model class has seen many ...

Understanding how and why certain communities bear a disproportionate burden of disease is challenging due to the scarcity of data on these communities. Surveys provide a useful avenue for accessing hard‐to‐reach populations, as many surveys ...

Visual summary of three Bayesian survey methodology approaches explored in this review, with potential applications in health disparities research. image image

Health systems are placing increasing emphasis on improving the design and operation of clinical trials with the aim of making the health technology adoption process more value-based. We present a model of a value-based, two-armed clinical trial in which ...

Decision makers often ask experts to forecast a future state. Experts, however, can be biased. In the economics and psychology literature, one extensively studied behavioral bias is called herding. Under strong levels of herding, disclosure of public ...

Many researchers and practitioners find statistics confusing. This course aims to give attendees an understanding of the meaning of the various statistics they see in papers or need to use in their own work. The course builds on the instructor’s ...

Transparent research practices enable the research design, materials, analytic methods, and data to be thoroughly evaluated and potentially reproduced. The HCI community has recognized research transparency as one quality aspect of paper submission and ...

We propose a variational inference-based framework for training a Gaussian process regression model subject to censored observational data. Data censoring is a typical problem encountered during the data gathering procedure and requires ...

Various noise models have been developed in quantum computing study to describe the propagation and effect of the noise that is caused by imperfect implementation of hardware. Identifying parameters such as gate and readout error rates is critical to ...

Multiple-try Metropolis (MTM) is a popular Markov chain Monte Carlo method with the appealing feature of being amenable to parallel computing. At each iteration, it samples several candidates for the next state of the Markov chain and randomly selects ...

Gaussian processes are powerful non-parametric probabilistic models for stochastic functions. However, the direct implementation entails a complexity that is computationally intractable when the number of observations is large, especially when ...

Bayesian data analysis is gaining traction in many fields, including empirical studies in software engineering. Bayesian approaches provide many advantages over traditional, or frequentist, data analysis, but the mechanics often remain opaque to beginners ...

An ensemble updating method for categorical state vectors is proposed. The method is based on a Bayesian view of the ensemble Kalman filter (EnKF). In the EnKF, Gaussian approximations to the forecast and filtering distributions are introduced, ...

This paper concerns differentially private Bayesian estimation of the parameters of a population distribution, when a noisy statistic of a sample from that population is shared to provide differential privacy. This work mainly addresses two ...