Results 261 to 270 of about 876,447 (311)

Polarity-Driven Selective Adsorption of Quercetin on Kaolinite: An Integrated DFT and Monte Carlo Study. [PDF]

Materials (Basel)
Ayad A, Harrou A, El Himri A, Benali M, Dira A, Aparicio S, Gutiérrez A, Soldera A, Gharibi E.   +8 more
europepmc   +1 more source

Estimating area under the curve from graph-derived summary data: a systematic comparison of standard and Monte Carlo approaches. [PDF]

BMC Med Res Methodol
Titensor S, Ebbert J, Corte KD, Corte DD.   +3 more
europepmc   +1 more source

Bayesian estimation of the inverse Exponential Power distribution for COVID-19 case fatality analysis under SDG 3. [PDF]

Sci Rep
Akdam N, Alamri OA, Dutta S, Çiftci F, İyit N.   +4 more
europepmc   +1 more source

Model-based assessment of VRC07-523LS dosing in infants through population pharmacokinetic -pharmacodynamic modelling in adults and infants. [PDF]

J Antimicrob Chemother
Huynh D, Nikanjam M, Cunningham CK, McFarland EJ, Muresan P, Perlowski C, Yin DE, Moye J, Spiegel H, Gama L, Gaudinski M, Capparelli EV.   +11 more
europepmc   +1 more source

Therapeutic drug monitoring of amikacin in Chinese premature infant: a population pharmacokinetic analysis and dosage optimization. [PDF]

BMC Infect Dis
Li JH, Xu JH, Huang Y, Zhou M, Yang ZM, Li JJ, Feng ZT, Zhang Q, Yu YX, Duan LF, Tang L.   +10 more
europepmc   +1 more source

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Marginalized population Monte Carlo

2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
Population Monte Carlo is a statistical method that is used for generation of samples approximately from a target distribution. The method is iterative in nature and is based on the principle of importance sampling. In this paper, we show that in problems where some of the parameters are conditionally linear on the remaining parameters, we can improve ...
M. Bugallo, Mingyi Hong, P. Djurić
semanticscholar   +2 more sources

Population Monte Carlo schemes with reduced path degeneracy

2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017
Population Monte Carlo (PMC) algorithms are versatile adaptive tools for approximating moments of complicated distributions. A common problem of PMC algorithms is the so-called path degeneracy; the diversity in the adaptation is endangered due to the ...
V. Elvira, Luca Martino, D. Luengo, M. Bugallo   +3 more
semanticscholar   +3 more sources

Population Monte Carlo Algorithm in High Dimensions

Methodology and Computing in Applied Probability, 2011
For sampling from a multi-dimensional target distribution \(\pi\) , the population Monte Carlo (PMC) algorithm, is an iterative importance sampling such that the updated step is re-sampled from the previous sample by using the importance weight (normalized ratio of the previous importance function valued at the entries of the previous sample) and the ...
J. Lee, R. McVinish, K. Mengersen
semanticscholar   +4 more sources

Enhanced Mixture Population Monte Carlo Via Stochastic Optimization and Markov Chain Monte Carlo Sampling

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
The population Monte Carlo (PMC) algorithm is a popular adaptive importance sampling (AIS) method used for approximate computation of intractable integrals. Over the years, many advances have been made in the theory and implementation of PMC schemes.
Yousef El-Laham, Petar M. Djuric, Monica F. Bugallo   +2 more
openaire   +1 more source