Quality evaluation of linear inequality constrained estimation by Monte Carlo sampling in parameter space

Survey Review ◽  
2020 ◽  
pp. 1-7
Author(s):  
Songlin Zhang ◽  
Jingchang Li ◽  
Kun Zhang ◽  
Jie Han
Keyword(s):  
Monte Carlo ◽  
Parameter Space ◽  
Linear Inequality ◽  
Quality Evaluation ◽  
Monte Carlo Sampling ◽  
Constrained Estimation

scholarly journals Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Pedram Tavadze ◽  
Reese Boucher ◽  
Guillermo Avendaño-Franco ◽  
Keenan X. Kocan ◽  
Sobhit Singh ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Physical Properties ◽  
Parameter Space ◽  
Density Functional ◽  
Monte Carlo Sampling ◽  
Strongly Correlated ◽  
Bayesian Calibration ◽  
Iron Based

AbstractThe density-functional theory is widely used to predict the physical properties of materials. However, it usually fails for strongly correlated materials. A popular solution is to use the Hubbard correction to treat strongly correlated electronic states. Unfortunately, the values of the Hubbard U and J parameters are initially unknown, and they can vary from one material to another. In this semi-empirical study, we explore the U and J parameter space of a group of iron-based compounds to simultaneously improve the prediction of physical properties (volume, magnetic moment, and bandgap). We used a Bayesian calibration assisted by Markov chain Monte Carlo sampling for three different exchange-correlation functionals (LDA, PBE, and PBEsol). We found that LDA requires the largest U correction. PBE has the smallest standard deviation and its U and J parameters are the most transferable to other iron-based compounds. Lastly, PBE predicts lattice parameters reasonably well without the Hubbard correction.

scholarly journals Long-lived light neutralinos at Belle II

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Sourav Dey ◽  
Claudio O. Dib ◽  
Juan Carlos Helo ◽  
Minakshi Nayak ◽  
Nicolás A. Neill ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Parameter Space ◽  
Rare Decays ◽  
Final States ◽  
Charged Pions ◽  
Belle Ii ◽  
Background Events ◽  
Displaced Vertex

Abstract We consider light neutralinos of mass about 1 GeV, produced from τ lepton rare decays at Belle II, in the context of R-parity-violating (RPV) supersymmetry. With large and clean samples of τ leptons produced at the Belle II experiment, excellent sensitivity to such light neutralinos with the exotic signatures of displaced vertices is expected. We focus on two benchmark scenarios of single RPV operators, $$ {\lambda}_{311}^{\prime }{L}_3{Q}_1{\overline{D}}_1 $$ λ 311 ′ L 3 Q 1 D ¯ 1 and $$ {\lambda}_{312}^{\prime }{L}_3{Q}_1{\overline{D}}_2 $$ λ 312 ′ L 3 Q 1 D ¯ 2 , which induce both the production and decay of the lightest neutralino. For the reconstruction of a displaced vertex, we require at least two charged pions in the final states. We perform Monte-Carlo simulations for both signal and background events, and find that Belle II can explore regions in the parameter space competitive with other probes. In particular, for the $$ {\lambda}_{311}^{\prime } $$ λ 311 ′ scenario, it can put limits up to two orders of magnitude stronger than the current bounds.

Monte Carlo Sampling with Hierarchical Move Sets: POSH Monte Carlo

2009 ◽  
Vol 5 (8) ◽  
pp. 1968-1984 ◽  
Author(s):  
Jerome Nilmeier ◽  
Matthew P. Jacobson
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Sampling

Bayesian Estimation via Sequential Monte Carlo Sampling:  Unconstrained Nonlinear Dynamic Systems

2004 ◽  
Vol 43 (14) ◽  
pp. 4012-4025 ◽  
Author(s):  
Wen-shiang Chen ◽  
Bhavik R. Bakshi ◽  
Prem K. Goel ◽  
Sridhar Ungarala
Keyword(s):  
Monte Carlo ◽  
Bayesian Estimation ◽  
Dynamic Systems ◽  
Nonlinear Dynamic ◽  
Sequential Monte Carlo ◽  
Monte Carlo Sampling ◽  
Nonlinear Dynamic Systems
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