Non-Monte Carlo based failure rate estimates
A Multilevel Monte Carlo Method for Computing Failure Probabilities
... We consider the three Bernoulli distributions [Y = 1], [Y = −1], and [Y = 0] which have parameters p 1 , p −1 , and p 1 + p −1 , respectively ([·] is the Iverson bracket notation). These parameters can be used in ...
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A Monte Carlo Study of Efficiency Estimates from Frontier Models
... efficiency, based on an out-of-sample standard, but this interpretation would be wrong if the most efficient firm in the population were actually part of the ...
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A Monte Carlo Study of Ranked Efficiency Estimates from Frontier Models
... efficiency, based on parametric stochastic frontier ...mean estimates and the efficiency probabilities appear to be monotonically more precise as ...are based on the unitless mean absolute percentage ...
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Exact inference in diagnosing value-at-risk estimates: A Monte Carlo device
... 3 Monte Carlo test The test statistics (4) and (7) build upon processes that can be very easily constructed by simulation under the null hypothesis of a correct (dynamic) VaR ...Thus, Monte ...
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Monte Carlo Estimates of Evaluation Metric Error and Bias: Work in Progress
... the non-relevant ratings have a higher probability of being missing than the relevant ones, computing top-k hit rate (recall) using observed data is an unbiased estimator for the true value ...computing ...
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Monte Carlo-Based Tail Exponent Estimator
... α (based on the whole data set) and the estimated tail exponent. The tail exponent is estimated only on an arbitrarily chosen part of the data (Hill, 1975; Weron, 2001). Since extreme observations of prices on ...
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Numerical solutions of BSDEs : a-posteriori estimates and enhanced least-squares Monte Carlo
... convergence rate for the corresponding L 2 -error of order 1/2 in the number of times ...with rate N −1/2 when the sample size L grows proportional to N 3 K 2 , which is satisfied for l = ...
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Monte Carlo inference and maximization for phrase based translation
... 3.2 Translation performance For the experiments reported in this section, we used feature weights trained with minimum error rate training (MERT; Och, 2003) . Because MERT ignores the denominator in Equation 1, it ...
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A Monte Carlo based analysis of optimal design criteria
... parameter estimates ˆ θ are stored, and the process is repeated with new simulated data corresponding to the optimal mesh for M Monte Carlo ...
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Optimal Mortgage Refinancing Based on Monte Carlo Simulation
... Refinancing Based on Monte Carlo Simulation Jin Zheng, Siwei Gan, Xiaoxia Feng, and Dejun Xie Abstract—The pricing of mortgages in the context of stochas- tic interest rate plays an important ...
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Orthogonal Array Sampling for Monte Carlo Based Rendering
... Convergence Rate Even if we know that Monte Carlo integration will eventually converge to the correct answer, it is useful to know how quickly it will ...convergence rate. One thing that makes ...
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What causes the forecasting failure of Markov-Switching models? A Monte Carlo study
... linear autoregressive model is selected using the BIC criterion with a maximum lag length of 3. Finally, the predictions are computed into the two models at di¤erent horizons h = 1; : : : ; 8. The predictions are made in ...
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Continuous-time non-linear non-gaussian state-space modeling of electroencephalography with sequential Monte Carlo based estimation
... approximation based maximum likelihood approach and thus more practical for real biomedical signal processing ...gives estimates optimal in minimum mean-squared error (MMSE) ...
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Testing for (non)linearity in economic time series: a Monte Carlo comparison
... a failure to recognize and deal with the presence of nonlinearity in the generating mechanism of a time series can often lead to poorly behaved parameter estimates and to models which miss important serial ...
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Monte Carlo convolution for learning on non-uniformly sampled point clouds
... For the semantic segmentation task on real-world datasets, we use a similar architecture to the one used for the segmentation task. However, since the datasets used in this task are complete rooms of varying size, this ...
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A Hybrid Monte Carlo Sampling Filter for Non-Gaussian Data Assimilation
... improved estimates of the state of a dynamical system such as the ...is based on a Hybrid Monte Carlo (HMC) approach that can handle non-Gaussian probability ...of non-linearity ...
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Adjusted power estimates in Monte Carlo experiments
... Adjusted Power Estimates in Monte Carlo Experiments by.. Ji Zhang.[r] ...
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Non-linear Markov Chain Monte Carlo
... of non-linear Markov Chain Monte Carlo (MCMC) methods for simulating from a probability measure ...π. Non-linear Markov kernels ...such non-linear kernels often cannot be simulated ...
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Monte Carlo simulations of random non commutative geometries
... Random non-commutative geometries are introduced by integrat- ing over the space of Dirac operators that form a spectral triple with a fixed algebra and Hilbert ...using Monte Carlo simulations to ...
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Monte Carlo Simulation of Non-Local Transport Effects
... Keywords: Strained Si layer, electron velocity overshoot, conduction effective mass reduction,.. intervalley scattering rate reduction, Monte Carlo simulation.[r] ...
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