Automatic Relevance Determination for Identifying Thalamic Regions Implicated in Schizophrenia

Browne, Antony; Jakary, Angela; Vinogradov, Sophia; Fu, Yu; Deicken, Raymond F.

doi:10.1109/tnn.2008.2000203

Cited by 7 publications

(3 citation statements)

References 90 publications

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“…This is consistent with the multivariate embedding theorem's notion that different combinations of lags can produce equivalent predictions (Deyle & Sugihara, 2011). Thus, using the informed prior is not necessarily helpful if the goal is to improve predictions, though it may facilitate inference about ecological mechanisms based on the ARD selection of relevant lags (Browne et al., 2008; Marwala, 2015; Munch et al., 2018).…”

Section: Discussionmentioning

confidence: 99%

Leveraging spatial information to forecast nonlinear ecological dynamics

2020

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There has been a recent demand for forecasting in ecology, particularly in the field of ecosystem management. Empirical dynamic modelling (EDM), an equation‐free nonlinear forecasting method, is receiving growing attention, but it requires long time series to produce accurate predictions. Though most ecological time series are short, spatial replicates are often available. Here we explore how utilizing available spatial data can improve our ability to forecast ecological dynamics. There are several ways to incorporate spatial information into EDM and not all have been applied in ecology. We compare spatial EDM approaches used in ecology and physics and introduce a flexible Bayesian model that makes use of prior movement information. We test these methods on simulated data generated with three population dynamics models with varying levels of complexity, time series length, spatial symmetry and heterogeneity. Adding spatial data generally improves accuracy, though the best method depends on the spatial process. We applied the methods to empirical fisheries data, highlighting the complexity of real population dynamics. Leveraging spatial data is an effective way to overcome the problem of short ecological time series. Since the best forecasting method depends on the underlying dynamics, we suggest that users apply several in concert and that this may be useful in identifying spatial heterogeneity in dynamics.

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Section: Discussionmentioning

confidence: 99%

Leveraging spatial information to forecast nonlinear ecological dynamics

2020

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“…Automatic relevance determination (ARD) is a Bayesian method used to assess the importance of features. It can be applied to standard feed-forward neural networks [18,20,26] and has many applications [25,28,3]. This approach optimizes model evidence (marginal likelihood), the classic criterion for Bayesian modeling, and generates hyperparameters that represent the relevance of different input features.…”

Section: Mlp Neural Networkmentioning

confidence: 99%

A multilayer perceptron neural network-based approach for the identification of responsiveness to interferon therapy in multiple sclerosis patients

Calcagno¹,

Staiano²,

Fortunato³

et al. 2010

Information Sciences

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“…Digital Object Identifier 10.1109/TNNLS.2014.2328576 brain activity [14]- [16]. The ARD prior is a Gaussian distribution with mean zero and the inverse of the variance obey a conjugate prior distribution, a Gamma distribution with hyperparameters [17].…”

Section: Introductionmentioning

confidence: 99%

Variational Inference With ARD Prior for NIRS Diffuse Optical Tomography

Miyamoto

Watanabe

Ikeda

et al. 2015

IEEE Trans. Neural Netw. Learning Syst.

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Diffuse optical tomography (DOT) reconstructs 3-D tomographic images of brain activities from observations by near-infrared spectroscopy (NIRS) that is formulated as an ill-posed inverse problem. This brief presents a method for NIRS DOT based on a hierarchical Bayesian approach introducing the automatic relevance determination prior and the variational Bayes technique. Although the sparseness of the estimation strongly depends on the hyperparameters, in general, our method has less dependency on the hyperparameters. We confirm through numerical experiments that a schematic phase diagram of sparseness with respect to the hyperparameters has two regions: in one region hyperparameters give sparse solutions and in the other they give dense ones. The experimental results are supported by our theoretical analyses in simple cases.

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Automatic Relevance Determination for Identifying Thalamic Regions Implicated in Schizophrenia

Cited by 7 publications

References 90 publications

Leveraging spatial information to forecast nonlinear ecological dynamics

Leveraging spatial information to forecast nonlinear ecological dynamics

A multilayer perceptron neural network-based approach for the identification of responsiveness to interferon therapy in multiple sclerosis patients

Variational Inference With ARD Prior for NIRS Diffuse Optical Tomography

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