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Bayesian inference in satellite gravity inversion
Authors: K. I. Kis, P. T. Taylor, G. Wittmann, H. R. Kim, B. Toronyi and T. Mayer-Gürr

23 97 127 Box G E P, Tiao G C 1973: Bayesian Inference in Statistical Analysis. Addison-Wesley Publishing Company

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Bayesian inference to partition determinants of community dynamics from observational time series
Authors: C. M. Mutshinda, Z. V. Finkel, C. E. Widdicombe and A. J. Irwin


Ecological communities are shaped by a complex interplay between abiotic forcing, biotic regulation and demographic stochasticity. However, community dynamics modelers tend to focus on abiotic forcing overlooking biotic interactions, due to notorious challenges involved in modeling and quantifying inter-specific interactions, particularly for species-rich systems such as planktonic assemblages. Nevertheless, inclusive models with regard to the full range of plausible drivers are essential to characterizing and predicting community response to environmental changes. Here we develop a Bayesian model for identifying, from in-situ time series, the biotic, abiotic and stochastic factors underlying the dynamics of species-rich communities, focusing on the joint biomass dynamics of biologically meaningful groups. We parameterize a multivariate model of population co-variation with an explicit account for demographic stochasticity, density-dependent feedbacks, pairwise interactions, and abiotic stress mediated by changing environmental conditions and resource availability, and work out explicit formulae for partitioning the temporal variance of each group in its biotic, abiotic and stochastic components. We illustrate the methodology by analyzing the joint biomass dynamics of four major phytoplankton functional types namely, diatoms, dinoflagellates, coccolithophores and phytoflagellates at Station L4 in the Western English Channel using weekly biomass records and coincident measurements of environmental covariates describing water conditions and potentially limiting resources. Abiotic and biotic factors explain comparable amounts of temporal variance in log-biomass growth across functional types. Our results demonstrate that effective modelling of resource limitation and inter-specific interactions is critical for quantifying the relative importance of abiotic and biotic factors.

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Bayesian inference and model choice for Holling’s disc equation: a case study on an insect predator-prey system
Authors: N. E. Papanikolaou, H. Williams, N. Demiris, S. P. Preston, P. G. Milonas and T. Kypraios

Simulation for Bayesian Inference . Chapman and Hall/CRC , Boca Raton . Gelman , A. , G. Roberts and W. Gilks . 1996 . Efficient metropolis jumping rules . In Bernado , J

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. Cryptogamie, Bryologie 26 131 150 Huelsenbeck, J. P. and Ronquist, F. (2001): MRBAYES: Bayesian inference of

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This paper is the first report on the molecular characterisation of myxozoan parasites from the odontobutid fish Chinese (Amur) sleeper (Perccottus glenii Dybowski, 1877). The authors determined the partial 18S rDNA sequence of Myxidium shedkoae Sokolov, 2013 from the gallbladder of the fish. Phylogenies reconstructed using maximum likelihood and Bayesian inference analysis revealed that M. shedkoae belongs to the hepatic biliary group of myxozoans (after Kristmundsson and Freeman, 2013) as a member of the clade consisting of Zschokkella sp. KLT-2014, Myxidium truttae and Zschokkella nova. Some new morphological features of the parasite are also presented.

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A survey of product posterior distributions
Author: Saralees Nadarajah

References 1 A bdullah , M. Y. , Bayesian inferences with the poly-t distribution , Ph.D. Thesis, Oklahoma State University , USA ( 1982

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): Bayesian Inference and Decision Techniques . Amsterdam: North-Holland, pp. 233–243. Zellner A. Bayesian Inference and Decision Techniques

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Embedded neural controllers based on spiking neuron models
Authors: László BakÓ and Sándor Brassai

Deneve S. Bayesian inference in spiking neurons, Ed. by L. K. Saul, Y. Weiss, L. Bottou, Advances in Neural Information Processing Systems , Vol. 17, MIT Press, Cambridge, MA, 2005, pp. 353–360. Deneve S

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The other kind of perceptual learning
Author: József Fiser

. D. Kersten P. Mamassian A. Yuille 2004 Object perception as Bayesian inference

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. T. S. Lee D. Mumford 2003 Hierarchical Bayesian inference in the visual cortex Journal of the Optical Society of America

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