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Relevant bibliographies by topics / Bayesian Moment Matching

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Journal articles

Academic literature on the topic 'Bayesian Moment Matching'

Author: Grafiati

Published: 25 May 2024

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Journal articles on the topic "Bayesian Moment Matching"

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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