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We develop a novel online learning framework, leveraging variational inference and sequential Monte Carlo, which enables flexible and accurate Bayesian joint filtering. ... In a streaming setting where data are processed one sample at a time, simultaneous inference of the state and its nonlinear dynamics has posed significant challenges in practice. ... We refer to our approach as streaming variational Monte Carlo (SVMC). For clarity, we review SMC and VI in the follow sections. ...arXiv:1906.01549v4 fatcat:732d4mlz4bhjni3qp6yhjn55ca
This work demonstrates the use of Monte Carlo simulation in assessing the vulnerability of water networks to noisy mass loads. ... Monte Carlo allows the network options to be compared on the basis of robustness, or resistance to variations outside of design conditions. ... Future work on Monte Carlo simulation techniques should focus on expanded models wherein variations in other process parameters, such as stream flowrates and feed water quality, are taken into account. ...doi:10.1016/j.compchemeng.2006.11.005 fatcat:3474zpzrlvbtzn5xglydpyhtz4
This paper presents results derived from Monte Carlo simulations un- dertaken to quantify the effect of input data uncertainty (Type II error) on the estimation of in-stream pollutant concentration. ... That is, Monte Carlo results describe expected variability in the system. ...
The aim of this paper is to use Monte Carlo simulation to analyse the effect of stream data variation on the economic performance of retrofit designs. ... but can also favour a different design to that favoured by the Monte Carlo results. ... Monte Carlo simulation is a stochastic tool for design, as opposed to deterministic methods, such as mixed integer linear programming, and is therefore naturally suited to problems where variation in data ...doi:10.3303/cet1870170 doaj:52b703d85fde4567929da7b0940c35d9 fatcat:gekkp7egnjdnzff6wr3syd7rdm
Journal of Hydrology
In this study, we use a Monte Carlo ther-mal mixing model to predict the thermal impact of removing a 700-m-long culvert. ... We calculate the daily-average groundwater discharge velocity at 15 locations in the stream using signal decay in streambed temperatures, and utilize a Monte Carlo implementation of the hetero-geneous ... We assume with this method that the baseflow variations arise from streambed material heterogeneity and local variations in gradient; however, the Monte Carlo sensitivity simulations also suggest upper ...doi:10.1016/j.jhydrol.2010.08.030 fatcat:aeelahxx2vbebpg5mxcecrrie4
Markov chain Monte Carlo is a widely-used technique for generating a dependent sequence of samples from complex distributions. ... In this paper we show how to modify some commonly used Markov chains to use a dependent stream of random numbers in place of independent uniform variates. ... Discussion We have shown that arbitrary streams of numbers can be used to drive a Markov chain Monte Carlo algorithm that leaves a target distribution invariant. ...arXiv:1204.3187v1 fatcat:7nccfw26ungejka6gnzygnknku
@ Monte-Carlo —— Jensen, eqn (17c) This Study, eqn (17b) 10 15 20 @ Monte-Carlo —— Jensen, eqn (17c) This Study, eqn (17b) 10 15 20 Cy FIG. 2. ... Comparison of % Bias and % RMSE of Estimators A, and H, Using Monte Carlo and Analytical Approximation for Case n= 1,000 ...
A probabilistic simulation model, based on the Monte Carlo method, is developed and implemented for this purpose. ... However, through any fabrication process, the geometric parameters present slight, inherent variation from the designed values than might affect the performance of the micromixer. ... Once the variation laws of the geometrical parameters are defined, the Monte Carlo simulation can be applied [18, 19] . ...doi:10.1155/2015/343087 fatcat:cj6goac7sjhulcimjl5mth32iu
In this paper we show that the Condensed History Algorithm can be justified as a Monte Carlo simulation of an operator-split procedure in which the streaming, angular scattering, and slowing-down operators ... Although the Condensed History Algorithm is a successful and widely-used Monte Carlo method for solving electron transport problems, it has been derived only by an ad-hoc process based on physical reasoning ... but without streaming or angular scattering) can each be simulated by a relatively simple Monte Carlo method. ...doi:10.1016/0306-4549(92)90013-2 fatcat:w7fdikudmjhong22hejosebxm4
Using Monte Carlo Simulation, inflexibility has been defined as the probability of a HEN to fail to meet temperature targets within defined tolerances. ... The aim of this paper is to use Monte Carlo Simulation (MCS) to conduct an analysis of the flexibility and controllability of retrofitted Heat Exchanger Network (HEN) designs. ... Model the starting HEN and run the Monte Carlo Simulation. 3. Calculate the network inflexibility. 4. Conduct the retrofit analysis and run the Monte Carlo Simulation for the retrofitted HENs. 5. ...doi:10.3303/cet1976078 doaj:0a0ee48a56be40f6b497a81de3a29ced fatcat:gfkhtejqt5hidmaftoexgoemci
MeTHODOLOGY DEVELOPMENT The classic Streeter—-Phelps equation shown below was assumed to perfectly describe in-stream spatial variation in dissolved oxygen: K, Lo a d in which D = dissolved oxygen deficit ... The Monte Carlo technique requires knowledge of the frequency distribution of each independent variable. ...
Monte Carlo simulations are increasingly used in medical physics. ... But, the main drawback of Monte Carlo simulations is their high computing time. ... Furthermore, Monte Carlo calculations are used to compute model-dependant parameters such as the variation of distribution dose with angle around a source (e.g the anisotropy functions). 1.1.b Modelling ...doi:10.1142/s0129626404001829 fatcat:7gcp27jno5apxhim5w7u6xzycy
Sensitivity of denitrification to discharge, temperature and nitrogen concentration was determined by a Monte Carlo analysis. ... The objective of this contribution is to analyze variation of these variables, on in-stream denitrification, their seasonal variation along the year and the impact of river morphology on N-retention for ... Sensitivity of denitrification to discharge, temperature and nitrogen concentration was determined by a Monte Carlo analysis. ...doi:10.1007/s10584-011-0369-1 fatcat:ff42blfllnhqhpcoowextfapni
In conducting the Monte Carlo procedure, the statistical properties of the model parameter used throughout this study are listed in Table 1. ... ESTIMATION OF THE TRUE DISTRIBUTION OF D BY Monte CARLO SIMULATION It was assumed ir this study that the DO deficit at any downstream lo- cation x can be computed using Eq. 7. ...
In this work, a Monte Carlo simulation approach is proposed to assess the vulnerability of bioenergy parks to variable capacity disruptions. ... Variation in the magnitude of disruption per scenario is simulated using Monte Carlo. ... A Monte Carlo simulation is then used to determine the effect of disruption variations in plant capacities and final output of the bioenergy park. ...doi:10.3303/cet1756080 doaj:8e7a195f5c7341fe903d437e9179e77f fatcat:5w2jn4bbevh3fdaz73w3t2pniy
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