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Journal articles on the topic 'Bayesian Moment Matching'

Author: Grafiati

Published: 25 May 2024

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1

Zhang, Qiong, and Yongjia Song. "Moment-Matching-Based Conjugacy Approximation for Bayesian Ranking and Selection." ACM Transactions on Modeling and Computer Simulation 27, no. 4 (2017): 1–23. http://dx.doi.org/10.1145/3149013.

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2

Franke, Reiner, Tae-Seok Jang, and Stephen Sacht. "Moment matching versus Bayesian estimation: Backward-looking behaviour in a New-Keynesian baseline model." North American Journal of Economics and Finance 31 (January 2015): 126–54. http://dx.doi.org/10.1016/j.najef.2014.11.001.

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3

Cao, Zhixing, and Ramon Grima. "Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data." Journal of The Royal Society Interface 16, no. 153 (2019): 20180967. http://dx.doi.org/10.1098/rsif.2018.0967.

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Bayesian and non-Bayesian moment-based inference methods are commonly used to estimate the parameters defining stochastic models of gene regulatory networks from noisy single cell or population snapshot data. However, a systematic investigation of the accuracy of the predictions of these methods remains missing. Here, we present the results of such a study using synthetic noisy data of a negative auto-regulatory transcriptional feedback loop, one of the most common building blocks of complex gene regulatory networks. We study the error in parameter estimation as a function of (i) number of cel

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4

Nakagawa, Tomoyuki, and Shintaro Hashimoto. "On Default Priors for Robust Bayesian Estimation with Divergences." Entropy 23, no. 1 (2020): 29. http://dx.doi.org/10.3390/e23010029.

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This paper presents objective priors for robust Bayesian estimation against outliers based on divergences. The minimum γ-divergence estimator is well-known to work well in estimation against heavy contamination. The robust Bayesian methods by using quasi-posterior distributions based on divergences have been also proposed in recent years. In the objective Bayesian framework, the selection of default prior distributions under such quasi-posterior distributions is an important problem. In this study, we provide some properties of reference and moment matching priors under the quasi-posterior dis

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5

Yiu, A., R. J. B. Goudie, and B. D. M. Tom. "Inference under unequal probability sampling with the Bayesian exponentially tilted empirical likelihood." Biometrika 107, no. 4 (2020): 857–73. http://dx.doi.org/10.1093/biomet/asaa028.

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Summary Fully Bayesian inference in the presence of unequal probability sampling requires stronger structural assumptions on the data-generating distribution than frequentist semiparametric methods, but offers the potential for improved small-sample inference and convenient evidence synthesis. We demonstrate that the Bayesian exponentially tilted empirical likelihood can be used to combine the practical benefits of Bayesian inference with the robustness and attractive large-sample properties of frequentist approaches. Estimators defined as the solutions to unbiased estimating equations can be

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6

Dimas, Christos, Vassilis Alimisis, Nikolaos Uzunoglu, and Paul P. Sotiriadis. "A Point-Matching Method of Moment with Sparse Bayesian Learning Applied and Evaluated in Dynamic Lung Electrical Impedance Tomography." Bioengineering 8, no. 12 (2021): 191. http://dx.doi.org/10.3390/bioengineering8120191.

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Dynamic lung imaging is a major application of Electrical Impedance Tomography (EIT) due to EIT’s exceptional temporal resolution, low cost and absence of radiation. EIT however lacks in spatial resolution and the image reconstruction is very sensitive to mismatches between the actual object’s and the reconstruction domain’s geometries, as well as to the signal noise. The non-linear nature of the reconstruction problem may also be a concern, since the lungs’ significant conductivity changes due to inhalation and exhalation. In this paper, a recently introduced method of moment is combined with

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7

Heath, Anna, Ioanna Manolopoulou, and Gianluca Baio. "Estimating the Expected Value of Sample Information across Different Sample Sizes Using Moment Matching and Nonlinear Regression." Medical Decision Making 39, no. 4 (2019): 347–59. http://dx.doi.org/10.1177/0272989x19837983.

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Background. The expected value of sample information (EVSI) determines the economic value of any future study with a specific design aimed at reducing uncertainty about the parameters underlying a health economic model. This has potential as a tool for trial design; the cost and value of different designs could be compared to find the trial with the greatest net benefit. However, despite recent developments, EVSI analysis can be slow, especially when optimizing over a large number of different designs. Methods. This article develops a method to reduce the computation time required to calculate

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8

Browning, Alexander P., Christopher Drovandi, Ian W. Turner, Adrianne L. Jenner, and Matthew J. Simpson. "Efficient inference and identifiability analysis for differential equation models with random parameters." PLOS Computational Biology 18, no. 11 (2022): e1010734. http://dx.doi.org/10.1371/journal.pcbi.1010734.

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Heterogeneity is a dominant factor in the behaviour of many biological processes. Despite this, it is common for mathematical and statistical analyses to ignore biological heterogeneity as a source of variability in experimental data. Therefore, methods for exploring the identifiability of models that explicitly incorporate heterogeneity through variability in model parameters are relatively underdeveloped. We develop a new likelihood-based framework, based on moment matching, for inference and identifiability analysis of differential equation models that capture biological heterogeneity throu

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9

Habibi, Reza. "Conditional Beta Approximation: Two Applications." Indonesian Journal of Mathematics and Applications 2, no. 1 (2024): 9–23. http://dx.doi.org/10.21776/ub.ijma.2024.002.01.2.

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Suppose that X,Y are two independent positive continuous random variables. Let P=\frac{X}{X+Y} and Z=X+Y. If X, Y have gamma distributions with the same scale parameter, then P distribution will be beta and P,\ Z are independent. In the case that the distributions of these two variables are not gamma, the P distribution is well approximated by the beta distribution. However, P,\ Z are dependent. According to matching moment method, it is necessary to compute the moments of conditional distribution for beta fitting. In this paper, some new methods for computing moments of conditional distributi

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10

Lu, Chi-Ken, and Patrick Shafto. "Conditional Deep Gaussian Processes: Empirical Bayes Hyperdata Learning." Entropy 23, no. 11 (2021): 1387. http://dx.doi.org/10.3390/e23111387.

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It is desirable to combine the expressive power of deep learning with Gaussian Process (GP) in one expressive Bayesian learning model. Deep kernel learning showed success as a deep network used for feature extraction. Then, a GP was used as the function model. Recently, it was suggested that, albeit training with marginal likelihood, the deterministic nature of a feature extractor might lead to overfitting, and replacement with a Bayesian network seemed to cure it. Here, we propose the conditional deep Gaussian process (DGP) in which the intermediate GPs in hierarchical composition are support

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11

Heilbron, Micha, Jorie van Haren, Peter Hagoort, and Floris P. de Lange. "Lexical Processing Strongly Affects Reading Times But Not Skipping During Natural Reading." Open Mind 7 (2023): 757–83. http://dx.doi.org/10.1162/opmi_a_00099.

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Abstract In a typical text, readers look much longer at some words than at others, even skipping many altogether. Historically, researchers explained this variation via low-level visual or oculomotor factors, but today it is primarily explained via factors determining a word’s lexical processing ease, such as how well word identity can be predicted from context or discerned from parafoveal preview. While the existence of these effects is well established in controlled experiments, the relative importance of prediction, preview and low-level factors in natural reading remains unclear. Here, we

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12

Lu, Chi-Ken, and Patrick Shafto. "Conditional Deep Gaussian Processes: Multi-Fidelity Kernel Learning." Entropy 23, no. 11 (2021): 1545. http://dx.doi.org/10.3390/e23111545.

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Deep Gaussian Processes (DGPs) were proposed as an expressive Bayesian model capable of a mathematically grounded estimation of uncertainty. The expressivity of DPGs results from not only the compositional character but the distribution propagation within the hierarchy. Recently, it was pointed out that the hierarchical structure of DGP well suited modeling the multi-fidelity regression, in which one is provided sparse observations with high precision and plenty of low fidelity observations. We propose the conditional DGP model in which the latent GPs are directly supported by the fixed lower

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13

Amini Farsani, Zahra, and Volker J. Schmid. "Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis." Entropy 24, no. 2 (2022): 155. http://dx.doi.org/10.3390/e24020155.

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Background: For the kinetic models used in contrast-based medical imaging, the assignment of the arterial input function named AIF is essential for the estimation of the physiological parameters of the tissue via solving an optimization problem. Objective: In the current study, we estimate the AIF relayed on the modified maximum entropy method. The effectiveness of several numerical methods to determine kinetic parameters and the AIF is evaluated—in situations where enough information about the AIF is not available. The purpose of this study is to identify an appropriate method for estimating

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14

Pan, Yuangang, Ivor W. Tsang, Yueming Lyu, Avinash K. Singh, and Chin-Teng Lin. "Online Mental Fatigue Monitoring via Indirect Brain Dynamics Evaluation." Neural Computation 33, no. 6 (2021): 1616–55. http://dx.doi.org/10.1162/neco_a_01382.

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Driver mental fatigue leads to thousands of traffic accidents. The increasing quality and availability of low-cost electroencephalogram (EEG) systems offer possibilities for practical fatigue monitoring. However, non-data-driven methods, designed for practical, complex situations, usually rely on handcrafted data statistics of EEG signals. To reduce human involvement, we introduce a data-driven methodology for online mental fatigue detection: self-weight ordinal regression (SWORE). Reaction time (RT), referring to the length of time people take to react to an emergency, is widely considered an

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15

Wang, Yilin, and Baokuan Chang. "Extraction of Human Motion Information from Digital Video Based on 3D Poisson Equation." Advances in Mathematical Physics 2021 (December 28, 2021): 1–11. http://dx.doi.org/10.1155/2021/1268747.

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Based on the 3D Poisson equation, this paper extracts the features of the digital video human body action sequence. By solving the Poisson equation on the silhouette sequence, the time and space features, time and space structure features, shape features, and orientation features can be obtained. First, we use the silhouette structure features in three-dimensional space-time and the orientation features of the silhouette in three-dimensional space-time to represent the local features of the silhouette sequence and use the 3D Zernike moment feature to represent the overall features of the silho

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16

Chin, Kuo-Hsuan, and Tzu-Yun Huang. "An Empirical Study of Taiwan’s Real Business Cycle." International Journal of Economics and Finance 10, no. 2 (2018): 124. http://dx.doi.org/10.5539/ijef.v10n2p124.

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We study the characteristics of the real business cycle and the sources of the economic fluctuation in Taiwan over the last forty years, when it experienced both developing and developed stages of the economy, by considering a small open economy real business cycle model with financial friction. In particular, the breaking time point that distinguishes between developing and developed stages of the economy in Taiwan is chosen on the basis of the International Monetary Fund (IMF). We use a Bayesian approach to obtain the posterior densities for the structural parameters of interest. Conditionin

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17

Buder, S., K. Lind, M. K. Ness, et al. "The GALAH survey: An abundance, age, and kinematic inventory of the solar neighbourhood made with TGAS." Astronomy & Astrophysics 624 (April 2019): A19. http://dx.doi.org/10.1051/0004-6361/201833218.

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The overlap between the spectroscopic Galactic Archaeology with HERMES (GALAH) survey and Gaia provides a high-dimensional chemodynamical space of unprecedented size. We present a first analysis of a subset of this overlap, of 7066 dwarf, turn-off, and sub-giant stars. These stars have spectra from the GALAH survey and high parallax precision from the Gaia DR1 Tycho-Gaia Astrometric Solution. We investigate correlations between chemical compositions, ages, and kinematics for this sample. Stellar parameters and elemental abundances are derived from the GALAH spectra with the spectral synthesis

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18

Goedhart, S., W. D. Cotton, F. Camilo, et al. "The SARAO MeerKAT 1.3 GHz Galactic Plane Survey." Monthly Notices of the Royal Astronomical Society, May 3, 2024. http://dx.doi.org/10.1093/mnras/stae1166.

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Abstract We present the SARAO MeerKAT Galactic Plane Survey (SMGPS), a 1.3 GHz continuum survey of almost half of the Galactic Plane (251○ ≤l ≤ 358○ and 2○ ≤l ≤ 61○ at |b| ≤ 1${_{.}^{\circ}}$5). SMGPS is the largest, most sensitive and highest angular resolution 1 GHz survey of the Plane yet carried out, with an angular resolution of 8″ and a broadband RMS sensitivity of ∼10–20 μJy beam−1. Here we describe the first publicly available data release from SMGPS which comprises data cubes of frequency-resolved images over 908–1656 MHz, power law fits to the images, and broadband zeroth moment inte

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19

Bock, Andreas, and Colin J. Cotter. "Learning landmark geodesics using the ensemble Kalman filter." Foundations of Data Science, 2021, 0. http://dx.doi.org/10.3934/fods.2021020.

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<p style='text-indent:20px;'>We study the problem of diffeomorphometric geodesic landmark matching where the objective is to find a diffeomorphism that, via its group action, maps between two sets of landmarks. It is well-known that the motion of the landmarks, and thereby the diffeomorphism, can be encoded by an initial momentum leading to a formulation where the landmark matching problem can be solved as an optimisation problem over such momenta. The novelty of our work lies in the application of a derivative-free Bayesian inverse method for learning the optimal momentum encoding the d

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