Bayesian inference of a dynamical model evaluating Deltamethrin effect on Daphnia survival

2018 ◽  
Vol 2 ◽  
pp. 14
Author(s):  
Abdoulaye Diouf ◽  
Baba Issa Camara ◽  
Diene Ngom ◽  
Hela Toumi ◽  
Vincent Felten ◽  
...  
Keyword(s):  
Dynamical System ◽  
Bayesian Inference ◽  
Environmental Risks ◽  
Real Problem ◽  
Inference Algorithm ◽  
Inference Method ◽  
Toxic Compound ◽  
Bayesian Inference Method ◽  
Long Term Impact

The estimation of toxicokinetic and toxicodynamic (TK-TD) models parameters is a real problem in research. These models highlight a dynamics of  internalisation of a toxic compound and a dynamics of the damage that this contaminant will cause on an organism and of possible repairs on the latter. This coupling TK-TD makes it possible to connect these measurements at different times with the same set of parameters sometimes very important in number. In this paper, the focus is on assessing the long-term impact of deltamethrin effects on a sample of daphnia magna survival. For this purpose we fit our TK-TD model of deltamethrin bio-accumuluation and daphnia survival to our experimental data by using bayesian inference algorithm developed in the package deBinfer of R. This bayesian inference method allows estimate simultaneously all the parameters of deltamethrin bio-accumuluation and daphnia survival dynamical system. For the estimation of the environmental risks, our results show that whatever the concentration or duration of exposure, the concentration of bioaccumulated deltamerin should not exceed on average 6.188 ng/L, at the risk of observing the deadly effects

scholarly journals A Bayesian Inference Method Using Monte Carlo Sampling for Estimating the Number of Communities in Bipartite Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Guo-Zheng Wang ◽  
Li Xiong ◽  
Hu-Chen Liu
Keyword(s):  
Monte Carlo ◽  
Bayesian Inference ◽  
Probability Distribution ◽  
Community Detection ◽  
Degree Distribution ◽  
Prior Probability ◽  
Bipartite Networks ◽  
Inference Method ◽  
Prior Probability Distribution ◽  
Bayesian Inference Method

Community detection is an important analysis task for complex networks, including bipartite networks, which consist of nodes of two types and edges connecting only nodes of different types. Many community detection methods take the number of communities in the networks as a fixed known quantity; however, it is impossible to give such information in advance in real-world networks. In our paper, we propose a projection-free Bayesian inference method to determine the number of pure-type communities in bipartite networks. This paper makes the following contributions: (1) we present the first principle derivation of a practical method, using the degree-corrected bipartite stochastic block model that is able to deal with networks with broad degree distributions, for estimating the number of pure-type communities of bipartite networks; (2) a prior probability distribution is proposed over the partition of a bipartite network; (3) we design a Monte Carlo algorithm incorporated with our proposed method and prior probability distribution. We give a demonstration of our algorithm on synthetic bipartite networks including an easy case with a homogeneous degree distribution and a difficult case with a heterogeneous degree distribution. The results show that the algorithm gives the correct number of communities of synthetic networks in most cases and outperforms the projection method especially in the networks with heterogeneous degree distributions.

Bayesian Inference of Information Theoretic Metrics of Anonymity

2014 ◽  
Vol 989-994 ◽  
pp. 4680-4683
Author(s):  
Han Ru Pei ◽  
Zhi Jian Wang ◽  
Yu Wang
Keyword(s):  
Bayesian Inference ◽  
Probability Distribution ◽  
Inference Method ◽  
Information Theoretic ◽  
System Parameters ◽  
Bayesian Inference Method

Information theoretic metrics is popular theory to measure anonymity. However the difficulty in getting the probability distribution of subjects hampers its practical usage. In this paper we propose a Bayesian inference method to tackle this problem. Our method makes it possible to compare the anonymity of different anonymous systems. We use this method to analyze Threshold Mix and point out different system parameters which do and do not have influence on anonymity.

On the Estimation of Directional Wave Spectrum Based on Stationary Vessels 1st Order Motions: A New Set of Experimental Results

2008 ◽  
Author(s):  
Joa˜o V. Sparano ◽  
Eduardo A. Tannuri ◽  
Alexandre N. Simos ◽  
Vini´cius L. F. Matos
Keyword(s):  
Bayesian Inference ◽  
Wave Spectrum ◽  
Small Scale ◽  
Large Set ◽  
Wave Spectra ◽  
Inference Method ◽  
Peak Period ◽  
Wave Basin ◽  
Bayesian Inference Method ◽  
Directional Wave

The practicability of estimating directional wave spectra based on a vessel 1st order response has been recently addressed by several researchers. The interest is justified since on-board estimations would only require only a simple set of accelerometers and rate-gyros connected to an ordinary PC. The on-board wave inference based on 1st order motions is therefore an uncomplicated and inexpensive choice for wave estimation if compared to wave buoys and radar systems. The latest works in the field indicate that it is indeed possible to obtain accurate estimations and a Bayesian inference model seems to be the preferable method adopted for performing this task. Nevertheless, most of the previous analysis has been based exclusively on numerical simulations. At Polytechnic School, an extensive research program supported by Petrobras has been conducted since 2000, aiming to evaluate the possibility of estimating wave spectrum on-board offshore systems, like FPSO platforms. In this context, a series of small-scale tests has been performed at the LabOceano wave basin, comprising long and short crested seas. A possible candidate for on-board wave estimation has been recently studied: a crane barge (BGL) used for launching ducts offshore Brazil. The 1:48 model has been subjected to bow and quartering seas with different wave heights and periods and also different levels of directional spreading. A Bayesian inference method was adopted for evaluating the wave spectra based on the time-series of motions and the results were directly compared to the wave spectra measured in the basin by means of an array of wave probes. Very good estimations of the statistical parameters (significant wave height, peak period and mean wave direction) were obtained and, in most cases, even the directional spreading could be properly predicted. Inversion of the mean direction (180° shift), mentioned by some authors as a possible drawback of the Bayesian inference method, was not observed in any case. Sensitivity analysis on errors in the input parameters, such as the vessel inertial characteristics, has also been performed and attested that the method is robust enough to cope well with practical uncertainties. Overall results once again indicate a good performance of the inference method, providing an important additional validation supported by a large set of model tests.

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