DRAM: Efficient adaptive MCMC

Haario, Heikki; Laine, Marko; Mira, Antonietta; Saksman, Eero

doi:10.1007/s11222-006-9438-0

Cited by 1,391 publications

(1,265 citation statements)

References 16 publications

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“…In this study we rely on Markov chain Monte Carlo techniques, which allow for a Bayesian estimation of parameter distributions and quantify predictive uncertainty through ensemble simulations. Additional information on the algorithms [Haario et al, 2006] can be found in the supporting information S2. Note that trends (both in GRACE and the reconstruction) are removed during model calibration ( Figure S1).…”

Section: Model Identificationmentioning

confidence: 99%

A global reconstruction of climate‐driven subdecadal water storage variability

Humphrey

Gudmundsson

Seneviratne

2017

Geophysical Research Letters

103

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Since 2002, the Gravity Recovery and Climate Experiment (GRACE) mission has provided unprecedented observations of global mass redistribution caused by hydrological processes. However, there are still few sources on pre‐2002 global terrestrial water storage (TWS). Classical approaches to retrieve past TWS rely on either land surface models (LSMs) or basin‐scale water balance calculations. Here we propose a new approach which statistically relates anomalies in atmospheric drivers to monthly GRACE anomalies. Gridded subdecadal TWS changes and time‐dependent uncertainty intervals are reconstructed for the period 1985–2015. Comparisons with model results demonstrate the performance and robustness of the derived data set, which represents a new and valuable source for studying subdecadal TWS variability, closing the ocean/land water budgets and assessing GRACE uncertainties. At midpoint between GRACE observations and LSM simulations, the statistical approach provides TWS estimates (doi:) that are essentially derived from observations and are based on a limited number of transparent model assumptions.

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Section: Model Identificationmentioning

confidence: 99%

A global reconstruction of climate‐driven subdecadal water storage variability

Humphrey

Gudmundsson

Seneviratne

2017

Geophysical Research Letters

103

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“…Adding this information will dramatically increase the number of variables on which selection must be made and is likely to make inferring γ more difficult. To address this we will likely require improved proposal distributions for γ , as has been used for continuous variables in Haario et al (2006), which account for the estimated posterior correlations between these latent inclusion parameters. A method to generate correlated binary variables has been proposed in Leisch et al (1988).…”

Section: Future Workmentioning

confidence: 99%

A sparse hierarchical Bayesian model for detecting relevant antigenic sites in virus evolution

et al. 2017

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Understanding how viruses offer protection against closely related emerging strains is vital for creating effective vaccines. For many viruses, multiple serotypes often co-circulate and testing large numbers of vaccines can be infeasible. Therefore the development of an in silico predictor of cross-protection between strains is important to help optimise vaccine choice. Here we present a sparse hierarchical Bayesian model for detecting relevant antigenic sites in virus evolution (SABRE) which can account for the experimental variability in the data and predict antigenic variability. The method uses spike and slab priors to identify sites in the viral protein which are important for the neutralisation of the virus. Using the SABRE method we are able to identify a number of key antigenic sites within several viruses, as well as providing estimates of significant changes in the evolutionary history of the serotypes. We show how our method outperforms alternative established methods; standard mixed effects models, the mixed effects LASSO, and the mixed effects elastic nets. We also propose novel proposal mechanisms for the Markov chain Monte Carlo simulations, which Keywords Spike and slab prior · Foot-and-mouth disease virus · Influenza virus · Antigenic variability · Bayesian hierarchical models · Mixed-effects models · LASSO · Markov chain Monte Carlo

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“…For model inference, we used Markov Chain Monte Carlo sampling, where samples were drawn from the posterior distribution using the delayed rejection-adaptive metropolis algorithm. 36 Twenty thousand samples were drawn from each parameter chain, and the 5,000 first samples were discarded as burn-in samples. Figure 3 shows simulation results for a conventional version of the oxygen transport model without incorporating the effect of CTH (panels A-C) (Conv ss model) and a version of the oxygen transport model that includes the effect of CTH (panels D-F) (CTH ss model).…”

Section: Simulations and Data Analysismentioning

confidence: 99%

The Effects of Transit Time Heterogeneity on Brain Oxygenation during Rest and Functional Activation

Rasmussen

Jespersen

Østergaard

2015

J Cereb Blood Flow Metab

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The interpretation of regional blood flow and blood oxygenation changes during functional activation has evolved from the concept of 'neurovascular coupling', and hence the regulation of arteriolar tone to meet metabolic demands. The efficacy of oxygen extraction was recently shown to depend on the heterogeneity of capillary flow patterns downstream. Existing compartment models of the relation between tissue metabolism, blood flow, and blood oxygenation, however, typically assume homogenous microvascular flow patterns. To take capillary flow heterogeneity into account, we modeled the effect of capillary transit time heterogeneity (CTH) on the 'oxygen conductance' used in compartment models. We show that the incorporation of realistic reductions in CTH during functional hyperemia improves model fits to dynamic blood flow and oxygenation changes acquired during functional activation in a literature animal study. Our results support earlier observations that oxygen diffusion properties seemingly change during various physiologic stimuli, and posit that this phenomenon is related to parallel changes in capillary flow patterns. Furthermore, our results suggest that CTH must be taken into account when inferring brain metabolism from changes in blood flow-or blood oxygenation-based signals. Keywords: blood oxygen level-dependent contrast; capillaries; capillary transit time heterogeneity; hemodynamics; neurovascular coupling INTRODUCTION Brain function depends critically on a steady supply of oxygen. During rest, the central nervous system receives 420% of the cardiac output, and consciousness is lost within seconds after circulatory arrest. Although functional activation is typically associated with a modest 10% to 30% increase in local cerebral metabolic rate of oxygen (CMRO 2 ), regional cerebral blood flow (CBF) typically increases by 20% to 80%, with δCBF/δCMRO 2 coupling ratios consistently larger than unity. 1,2 This functional hyperemia permits the localization of brain activity by imaging techniques such as positron emission tomography and functional magnetic resonance imaging. Today, arterial spin labeling functional magnetic resonance imaging and blood oxygen leveldependent (BOLD) functional magnetic resonance imaging are the preferred tools in human brain mapping. 3,4 The interpretation of regional blood flow and blood oxygenation changes during functional activation has evolved from the concept of 'neurovascular coupling': mechanisms that converge on cerebral arterioles to adjust CBF according to changing metabolic needs. The subsequent distribution of blood across the capillary bed, and the oxygen diffusion from the microcirculation to active cells, is extremely complex, and so far biophysical models have been unable to establish with certainty whether the increase in oxygen supply during functional hyperemia is matched to the increased metabolic demands. 5 Buxton and Frank 6 derived the so-called 'oxygen limitation model', which relates CBF and CMRO 2 through the flow diffusion

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DRAM: Efficient adaptive MCMC

Cited by 1,391 publications

References 16 publications

A global reconstruction of climate‐driven subdecadal water storage variability

A global reconstruction of climate‐driven subdecadal water storage variability

A sparse hierarchical Bayesian model for detecting relevant antigenic sites in virus evolution

The Effects of Transit Time Heterogeneity on Brain Oxygenation during Rest and Functional Activation

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