Connecting PM and MAP in Bayesian spectral deconvolution by extending exchange Monte Carlo method and using multiple data sets

Motonaka, Kimiko; Miyoshi, Seiji

doi:10.1016/j.neunet.2019.05.004

Cited by 3 publications

(4 citation statements)

References 10 publications

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“…In our study, we propose the REPMMH method, which combines the PMMH method with the replica exchange method [ 24 , 38 , 39 , 40 ] to improve the problem of initial value dependence in the PMMH method. By employing the REPMMH method, we estimate the marginal posterior distribution of parameters from the time-series observations.…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, we propose the replica exchange particle marginal Metropolis–Hastings (REPMMH) method, which combines the PMMH method with the replica exchange method [ 24 , 38 , 39 , 40 ] in order to improve the problem of initial value dependence in the PMMH method. Combining the replica exchange method with the PMMH method makes it possible to estimate the parameters governing the dynamics for very complex and nonlinear time-series data.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Distributions of Parameters in Nonlinear State Space Models with Replica Exchange Particle Marginal Metropolis–Hastings Method

Inoue

Hukushima

Omori

2022

Entropy

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Extracting latent nonlinear dynamics from observed time-series data is important for understanding a dynamic system against the background of the observed data. A state space model is a probabilistic graphical model for time-series data, which describes the probabilistic dependence between latent variables at subsequent times and between latent variables and observations. Since, in many situations, the values of the parameters in the state space model are unknown, estimating the parameters from observations is an important task. The particle marginal Metropolis–Hastings (PMMH) method is a method for estimating the marginal posterior distribution of parameters obtained by marginalization over the distribution of latent variables in the state space model. Although, in principle, we can estimate the marginal posterior distribution of parameters by iterating this method infinitely, the estimated result depends on the initial values for a finite number of times in practice. In this paper, we propose a replica exchange particle marginal Metropolis–Hastings (REPMMH) method as a method to improve this problem by combining the PMMH method with the replica exchange method. By using the proposed method, we simultaneously realize a global search at a high temperature and a local fine search at a low temperature. We evaluate the proposed method using simulated data obtained from the Izhikevich neuron model and Lévy-driven stochastic volatility model, and we show that the proposed REPMMH method improves the problem of the initial value dependence in the PMMH method, and realizes efficient sampling of parameters in the state space models compared with existing methods.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating Distributions of Parameters in Nonlinear State Space Models with Replica Exchange Particle Marginal Metropolis–Hastings Method

Inoue

Hukushima

Omori

2022

Entropy

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“…2, replicas at higher temperatures are more likely to accept samples with worse results than replicas at lower temperatures, and thus can be expected to escape from local minimum. The replica exchange method allows efficient search while avoiding being stuck in a local optima by exchanging samples within different temperatures [17]. Secondly, replicas at higher temperatures search the parameter space globally, and provide better candidate parameter space for replicas at lower temperatures by exchanging samples.…”

Section: Data-driven Framework For Estimating Spatial Electrical Prop...mentioning

confidence: 99%

“…In this paper, we propose a data-driven method based on the replica exchange method [16,17] to estimate spatial electrical properties in the multi-compartment model from membrane potentials observed incompletely. The multi-compartment model [18], one of the neuron models, is known to have spatial electrical properties and reproduce nonlinear dynamics in neurons with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Data-driven estimation of spatial electrical property of multi-compartment models with neuronal morphology by replica exchange Monte Carlo method

Nakano,

Majumdar,

Omori

2024

NOLTA

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One of the neuron models that simulate the electrical activity of neurons, the multicompartment model, has spatial electrical properties that control nonlinear spatiotemporal dynamics and can reproduce nonlinear electrical responses with high accuracy. However, it is difficult to determine the model parameters in multi-compartment models from membrane potentials, since unknown high dimensional parameters for spatial electrical property should be estimated using incomplete observation data. In this paper, we propose a data-driven method to estimate the spatial electrical properties in the multi-compartment model from membrane potentials observed incompletely. The proposed method employs the replica exchange method using prior information considering morphological smoothness to solve problems of the local optima in the solution space and incompleteness of observation data. We further verify the effectiveness of the proposed method by using simulation data obtained from realistic neuron models.

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Estimation of Neuronal Dynamics of Izhikevich Neuron Models from Spike-Train Data with Particle Markov Chain Monte Carlo Method

2021

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Connecting PM and MAP in Bayesian spectral deconvolution by extending exchange Monte Carlo method and using multiple data sets

Cited by 3 publications

References 10 publications

Estimating Distributions of Parameters in Nonlinear State Space Models with Replica Exchange Particle Marginal Metropolis–Hastings Method

Estimating Distributions of Parameters in Nonlinear State Space Models with Replica Exchange Particle Marginal Metropolis–Hastings Method

Data-driven estimation of spatial electrical property of multi-compartment models with neuronal morphology by replica exchange Monte Carlo method

Estimation of Neuronal Dynamics of Izhikevich Neuron Models from Spike-Train Data with Particle Markov Chain Monte Carlo Method

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