• # CensMixReg

• Referenced in 12 articles [sw21090]
• finite mixtures of multivariate Student-$t$ distributions. This approach allows us to model data with ... efficient, EM-type algorithm for conducting maximum likelihood estimation of the parameters. The algorithm ... variance of the multivariate truncated Student-$t$ distributions. Further, a general information-based method ... approximating the asymptotic covariance matrix of the estimators is also presented. Results obtained from...
• # DPpackage

• Referenced in 72 articles [sw10495]
• space of all regression functions. Unfortunately, posterior distributions ranging over function spaces are highly complex ... includes models for marginal and conditional density estimation, receiver operating characteristic curve analysis, interval-censored ... prior, and a general purpose Metropolis sampling algorithm. To maximize computational efficiency, the actual sampling...
• # MEAPCA

• Referenced in 1 article [sw34793]
• MEAPCA: a multi-population evolutionary algorithm based on PCA for multi-objective optimization. The simulated ... regularity model based multi-objective estimated distribution algorithm, namely, RM-MEDA that adopts a segmented...
• # otrimle

• Referenced in 15 articles [sw17889]
• improper uniform distribution covering the whole Euclidean space. Parameters are estimated by (pseudo) maximum likelihood ... This is fitted by a EM-type algorithm. See Coretto and Hennig (2015)
• # CIRFE

• Referenced in 3 articles [sw27640]
• distributed random fields estimator. The paper presents a communication efficient distributed algorithm, CIRFE ... example, when monitoring the high-dimensional distributed state of a large-scale infrastructure with...
• # dynesty

• Referenced in 9 articles [sw28387]
• algorithms that focus exclusively on posterior estimation while retaining Nested Sampling’s ability to estimate ... evidences and sample from complex, multi-modal distributions. We provide an overview of Nested Sampling ... extension to Dynamic Nested Sampling, the algorithmic challenges involved, and the various approaches taken...
• # ContaminatedMixt

• Referenced in 19 articles [sw21014]
• mixtures of multivariate contaminated normal distributions as a tool for robust clustering and classification under ... expectation-conditional maximization algorithm is adopted to obtain maximum likelihood parameter estimates, and likelihood-based ... popular mixtures of multivariate normal and t distributions, this approach also allows for automatic detection...
• # D-STEM

• Referenced in 7 articles [sw16413]
• missing data. Model estimation is based on the expectation maximization algorithm ... performed using a distributed computing environment to reduce computing time when dealing with large data ... sets. The estimated model is eventually used to dynamically map the variables over the geographic...
• # ABC-SubSim

• Referenced in 14 articles [sw10099]
• Beck, “Estimation of small failure probabilities in high dimensions by subset simulation”, Probabilistic Engrg. Mech ... observation space. The efficiency of the algorithm is demonstrated in two examples that illustrate some ... show that the proposed algorithm outperforms other recent sequential ABC algorithms in terms of computational ... posterior distribution. We also show that ABC-SubSim readily provides an estimate of the evidence...

• Referenced in 8 articles [sw09498]
• fits an adaptive mixture of Student-t distributions to the density of interest. Then, importance ... sampling or the independence chain Metropolis-Hastings algorithm is used to obtain quantities of interest ... importance or candidate density. The estimation procedure is fully automatic and thus avoids the time ... distribution. The second shows the relevance of the adaptive mixture procedure through the Bayesian estimation...
• # SBA

• Referenced in 24 articles [sw05242]
• generic sparse bundle adjustment that is distributed under the GNU General Public License (GPL). Bundle ... vision algorithm to obtain optimal 3D structure and motion (i.e. camera matrix) parameter estimates. Provided...
• # POMPUS

• Referenced in 9 articles [sw02625]
• POMPUS: An optimized EIT reconstruction algorithm. Electrical impedance tomography (EIT) is a non-inverse imaging ... imaged and a best estimate conductivity distribution. These optimal experiments can be derived from measurements ... boundary. We describe a reconstruction algorithm, known as POMPUS, which is based...
• # decon

• Referenced in 22 articles [sw11088]
• goal is to estimate the density or distribution function from contaminated data; (2) nonparametric regression ... estimators computationally more efficient in R, we adapt the ”Fast Fourier Transform” (FFT) algorithm...
• # MORET

• Referenced in 5 articles [sw24191]
• perturbation algorithms, source distribution convergence, statistical detection of stationarity, unbiased variance estimation and creation...
• # HOSIM

• Referenced in 8 articles [sw38402]
• sequential simulation process, where local conditional distributions are generated using weighted orthonormal Legendre polynomials, which ... However, the three-dimensional implementation of the algorithm is computationally very intensive. To address ... order of approximation used to estimate a conditional distribution and the number of data used ... negligible contributions and allows an efficient simulation algorithm to be developed. The current version...