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ModelFlux
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Enhanced PromptMonte Carlo methods, statistical sampling algorithm, pseudorandom numbers, stochastic process, random variable simulation, Markov chain Monte Carlo, Metropolis-Hastings algorithm, importance sampling, variance reduction techniques, efficiency optimization, convergence analysis, computational complexity, statistical inference, Bayesian estimation, machine learning applications, uncertainty quantification, rare event simulation, rarefaction, ensemble methods, parallel computing, distributed computing, GPU acceleration, OpenCL programming, C++ implementation, Python wrapper, statistical software packages, R interface, MATLAB toolbox, Julia package, Fortran subroutine, numerical integration, multidimensional integrals, high-dimensional spaces, probability distributions, non-standard distributions, truncated distributions, truncated normal distribution, Gibbs sampler, slice sampling, adaptive rejection sampling, hit-and-run algorithm, simulated annealing, genetic algorithms, particle swarm optimization, differential evolution, evolutionary strategies, ant colony optimization, cuckoo search algorithm, grey wolf optimizer, harmony search algorithm, whale optimization algorithm, biogeography-based optimization, krill herd algorithm, monarch butterfly optimization, jaya algorithm, moth-flame optimization, teaching-learning-based optimization, gravitational search algorithm, artificial bee colony, bat algorithm, krill herd, differential evolution strategy, firefly algorithm, monarch butterfly optimization algorithm, grasshopper optimization algorithm, flower pollination algorithm, salp swarm algorithm, krill herd algorithm, imperialist competitive algorithm, invasive weed optimization, hybrid metaheuristics, memetic algorithm, genetic programming, evolution strategies, differential evolution, particle swarm optimization, cultural algorithm, ant colony optimization, bee algorithm, crow search algorithm, water cycle algorithm, sine cosine algorithm, mean-variance mapping optimization, multi-objective optimization, constrained optimization, unconstrained optimization, global optimization, local optimization, derivative-free optimization, gradient-based optimization, stochastic gradient descent, quasi-newton methods, conjugate gradient method, BFGS algorithm, L-BFGS-B, trust region methods, line search methods, simplex algorithm, branch and bound, cutting plane algorithm, integer linear programming, mixed-integer linear programming, quadratic programming, semi-definite programming, second-order cone programming, semidefinite programming relaxation, robust optimization, chance-constrained programming, stochastic programming, two-stage stochastic programming, multistage stochastic programming, scenario-based optimization, robust optimization, risk management, decision under uncertainty, stochastic control theory, dynamic programming, reinforcement learning, deep reinforcement learning, policy gradients, actor-critic methods, Q-learning, SARSA, TD-error, temporal difference error, eligibility traces, value function approximation, neural networks, recurrent neural networks, long short-term memory, gated recurrent units, transformer models, graph neural networks, spatial-temporal graphs, graph convolutional networks, graph attention networks, graph autoencoders, graph generative adversarial networks, spectral clustering, community detection, network embedding, node classification, link prediction, graph regression, graph ranking, graph similarity measurement, graph matching, graph partitioning, graph clustering, community detection, node classification, edge classification, link prediction, graph regression, graph ranking, graph similarity measurement, graph matching, graph partitioning, spectral clustering, k-means++, hierarchical clustering, DBSCAN, OPTICS, density-based clustering, expectation-maximization algorithm, Gaussian mixture models, Dirichlet process mixture models, Bayesian nonparametrics, non-parametric Bayes, nonparametric regression, kernel smoothing,
