An ensemble Markov chain Monte Carlo method of assessing uncertainty of aerosol properties from lidar measurements of extinction and backscatter is presented. The method applies the Metropolis-Hastings algorithm to an ensemble of Markov chains. Candidates are drawn from a hybrid random walk/independence sampler random generator. The independence sampler is formed by analyzing the ensemble partw...

Ensemble is a representative technique for improving classification performance by combining a set of classifiers. It is required to maintain the diversity among base classifiers for effective ensemble. Conventional ensemble approaches construct various classifiers by estimating the similarity on the output patterns of them, and combine them with several fusion methods. Since they measure the s...

Cluster ensemble has been shown to be an effective thought of improving the accuracy and stability of single clustering algorithms. It consists of generating a set of partition results from a same data set and combining them into a final one. In this paper, we develop a novel cluster ensemble method named Cluster Ensemble algorithm using the Binary k-means and Spectral Clustering (CEBKSC). By u...

Non-linear state space models are a widely-used class of models for biological, economic, and physical processes. Fitting these models to observed data is a difficult inference problem that has no straightforward solution. We take a Bayesian approach to the inference of unknown parameters of a non-linear state model; this, in turn, requires the availability of efficient Markov Chain Monte Carlo...

To achieve well calibrated probabilistic forecasts, ensemble forecasts often need to be statistically post-processed. One recent ensemble-calibration method is extended logistic regression which extends the popular logistic regression to yield full probability distribution forecasts. Although the purpose of this method is to post-process ensemble forecasts, mostly only the ensemble mean is used...

As a result of the lack of the knowledge with regard to the statistical properties of the dynamic models and operational observations, as well as the computational burden related to the high dimensionality of the realistic data assimilation problems especially those complex nonlinear filtering problems, the ensemble Kalman filter scheme has been paid much more attention in recent years and has ...

Regional crop yield prediction is a vital component of national food security assessment. Data assimilation method which combines crop growth model and remotely sensed data has been proven the most potential method in regional crop production estimation. This paper takes Hengshui district as study area, WOFOST as crop model, MODIS-LAI as observation data to test and verify the efficiency of EnK...

We present a novel method that can be used to characterize the dynamics of a source neuronal population. A set of readout, regular spiking neurons, is connected to the population in such a way as to facilitate coding of information about the source in the relative firing phase of the readouts. We show that such a strategy is useful in revealing temporally structured processes in the firing of s...