arXiv subjects

Paul T. Brown

Publications and source records attributed to Paul T. Brown.

5 recordsLinked to original sources

arXiv2026

Data Findability, Governance, and Community Engagement for Māori Research Data Sovereignty

Māori data sovereignty (MDSov) has established important principles for recognising Māori rights and interests in data. Relatively little attention has been given to how these principles can be operationalised within research institutions who, as a function of their operations, collect and use Māori data. This paper introduces the concept of Māori Research Data Sovereignty (MRDSov), extending existing understandings of MDSov into the specific context of research data and the research data lifecycle. Drawing on Indigenous Data Sovereignty scholarship and research data management literature, we define Māori research data and position MRDSov as the application of MDSov principles to research data produced by, about, or for Māori. We argue that operationalising MRDSov requires three interdependent elements: data findability, data governance, and community engagement. Data findability enables Māori research data to be identified and contextualised; governance provides mechanisms through which Māori authority over research data can be exercised; and community engagement grounds both in enduring relationships with Māori communities. We further demonstrate how these elements collectively enact the principles of MDSov within institutional research settings, providing a practical pathway for aligning research data practices with Te Tiriti o Waitangi obligations and Indigenous data governance expectations. The paper contributes a conceptual framework for universities and other research organisations seeking to embed Māori authority, accountability, and relationships throughout the research data lifecycle.

cs.CY↗

arXiv2023

Māori algorithmic sovereignty: idea, principles, and use

Due to the emergence of data-driven technologies in Aotearoa New Zealand that use Māori data, there is a need for values-based frameworks to guide thinking around balancing the tension between the opportunities these create, and the inherent risks that these technologies can impose. Algorithms can be framed as a particular use of data, therefore data frameworks that currently exist can be extended to include algorithms. Māori data sovereignty principles are well-known and are used by researchers and government agencies to guide the culturally appropriate use of Māori data. Extending these principles to fit the context of algorithms, and re-working the underlying sub-principles to address issues related to responsible algorithms from a Māori perspective leads to the Māori algorithmic sovereignty principles. We define this idea, present the updated principles and subprinciples, and highlight how these can be used to decolonise algorithms currently in use, and argue that these ideas could potentially be used to developed Indigenised algorithms.

cs.CY↗

arXiv2019

A Novel Method of Marginalisation using Low Discrepancy Sequences for Integrated Nested Laplace Approximations

Recently, it has been shown that approximations to marginal posterior distributions obtained using a low discrepancy sequence (LDS) can outperform standard grid-based methods with respect to both accuracy and computational efficiency. This recent method, which we will refer to as LDS-StM, can also produce good approximations to multimodal posteriors. However, implementation of LDS-StM into integrated nested Laplace approximations (INLA), a methodology in which grid-based methods are used, is challenging. Motivated by this problem, we propose modifications to LDS-StM that improves the approximations and make it compatible with INLA, without sacrificing computational speed. We also present two examples to demonstrate that LDS-StM with modifications can outperform INLA's own grid approximation with respect to speed and accuracy. We also demonstrate the flexibility of the new approach for the approximation of multimodal marginals.

stat.CO↗

arXiv2019

On approximating the shape of one dimensional functions

Consider an $s$-dimensional function being evaluated at $n$ points of a low discrepancy sequence (LDS), where the objective is to approximate the one-dimensional functions that result from integrating out $(s-1)$ variables. Here, the emphasis is on accurately approximating the shape of such \emph{one-dimensional} functions. Approximating this shape when the function is evaluated on a set of grid points instead is relatively straightforward. However, the number of grid points needed increases exponentially with $s$. LDS are known to be increasingly more efficient at integrating $s$-dimensional functions compared to grids, as $s$ increases. Yet, a method to approximate the shape of a one-dimensional function when the function is evaluated using an $s$-dimensional LDS has not been proposed thus far. We propose an approximation method for this problem. This method is based on an $s$-dimensional integration rule together with fitting a polynomial smoothing function. We state and prove results showing conditions under which this polynomial smoothing function will converge to the true one-dimensional function. We also demonstrate the computational efficiency of the new approach compared to a grid based approach.

math.NA↗

arXiv2016

Improving Grid Based Bayesian Methods

In some cases, computational benefit can be gained by exploring the hyper parameter space using a deterministic set of grid points instead of a Markov chain. We view this as a numerical integration problem and make three unique contributions. First, we explore the space using low discrepancy point sets instead of a grid. This allows for accurate estimation of marginals of any shape at a much lower computational cost than a grid based approach and thus makes it possible to extend the computational benefit to a hyper parameter space with higher dimensionality (10 or more). Second, we propose a new, quick and easy method to estimate the marginal using a least squares polynomial and prove the conditions under which this polynomial will converge to the true marginal. Our results are valid for a wide range of point sets including grids, random points and low discrepancy points. Third, we show that further accuracy and efficiency can be gained by taking into consideration the functional decomposition of the integrand and illustrate how this can be done using anchored f-ANOVA on weighted spaces.

stat.CO↗