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Space-filling Latin hypercube designs based on randomization restrictions in factorial experiments
Latin hypercube designs (LHDs) with space-filling properties are widely used for emulating computer simulators. Over the last three decades, a wide spectrum of LHDs have been proposed withExpand
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Probabilistic Interpretations of Recurrent Neural Networks
In the presence of unsupervised sequential data, such as long passages of text with no labels, RNNs can model the generative process of the sequential data by learning to estimate the distribution ofExpand
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Projective Sparse Latent Space Network Models
We propose an adjustment to latent-space network models which allows the number edges to scale linearly with the number of nodes, to scale quadratically, or at any intermediate rate. Expand
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Overcoming the multiple‐testing problem when testing randomness
We propose a new method for overcoming the problem of adjusting for the multiple-testing problem in the context of testing random-number generators. We suggest that it is to be used in conjunctionExpand
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A Unified Approach to Factorial Designs with Randomization Restrictions
Abtsrcat Factorial designs are commonly used to assess the impact of factors and factor combinations in industrial and agricultural experiments. Though preferred, complete randomization of trials isExpand
A Bayesian Hierarchical Model for Evaluating Forensic Footwear Evidence.
We develop a new spatial point process model for accidental locations, developed within a hierarchical Bayesian framework, that allows us to pool information across large heterogeneous databases of shoes, leading to significantly better model fit. Expand
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Projective, Sparse, and Learnable Latent Position Network Models
When modeling network data using a latent position model, it is typical to assume that the nodes' positions are independently and identically distributed. However, this assumption implies the averageExpand
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Isomorphism Check Algorithm for Two-level Factorial Designs with Randomization Restrictions
Multistage factorial designs with randomization restrictions are often used for designing industrial experiments when complete randomization of their trials is impractical. Ranjan, Bingham and DeanExpand
DrSMC : a sequential Monte Carlo sampler for deterministic relationships on continuous random variables
In this thesis, I propose Deterministic relationship Sequential Monte Carlo (DrSMC), a new Monte Carlo method for continuous variables possessing deterministic constraints. Expand
Isomorphism Check for $2^n$ Factorial Designs with Randomization Restrictions
We present theoretical results and an efficient relabeling strategy to both construct, and check the isomorphism of, multi-stage factorial designs within a unified framework. Expand
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