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arXiv · 2111.07848

A Two-Dimensional Intrinsic Gaussian Markov Random Field for Blood Pressure Data

Maria-Zafeiria Spyropoulou·James Bentham
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Abstract

Many real-world phenomena are naturally bivariate. This includes blood pressure, which comprises systolic and diastolic levels. Here, we develop a Bayesian hierarchical model that estimates these values and their interactions simultaneously, using sparse data that vary substantially between countries and over time. A key element of the model is a two-dimensional second-order Intrinsic Gaussian Markov Random Field, which captures non-linear trends in the variables and their interactions. The model is fitted using Markov chain Monte Carlo methods, with a block Metropolis-Hastings algorithm providing efficient updates. Performance is demonstrated using simulated and real data.

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Maria-Zafeiria Spyropoulou, James Bentham. 2021-11-15. A Two-Dimensional Intrinsic Gaussian Markov Random Field for Blood Pressure Data. https://arxiv.org/abs/2111.07848

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