Bibliografie tematiche / Forecast probability density function

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Letteratura scientifica selezionata sul tema "Forecast probability density function"

Autore: Grafiati
Pubblicato: 30 maggio 2022
Ultima modifica: 31 maggio 2022

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Articoli di riviste sul tema "Forecast probability density function"

1

Denholm-Price, J. C. W. "Can an ensemble give anything more than Gaussian probabilities?" Nonlinear Processes in Geophysics 10, n. 6 (31 dicembre 2003): 469–75. http://dx.doi.org/10.5194/npg-10-469-2003.

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Abstract. Can a relatively small numerical weather prediction ensemble produce any more forecast information than can be reproduced by a Gaussian probability density function (PDF)? This question is examined using site-specific probability forecasts from the UK Met Office. These forecasts are based on the 51-member Ensemble Prediction System of the European Centre for Medium-range Weather Forecasts. Verification using Brier skill scores suggests that there can be statistically-significant skill in the ensemble forecast PDF compared with a Gaussian fit to the ensemble. The most significant increases in skill were achieved from bias-corrected, calibrated forecasts and for probability forecasts of thresholds that are located well inside the climatological limits at the examined sites. Forecast probabilities for more climatologically-extreme thresholds, where the verification more often lies within the tails or outside of the PDF, showed little difference in skill between the forecast PDF and the Gaussian forecast.
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Smith, Leonard A., Hailiang Du e Sarah Higgins. "Designing Multimodel Applications with Surrogate Forecast Systems". Monthly Weather Review 148, n. 6 (5 maggio 2020): 2233–49. http://dx.doi.org/10.1175/mwr-d-19-0061.1.

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Abstract Probabilistic forecasting is common in a wide variety of fields including geoscience, social science, and finance. It is sometimes the case that one has multiple probability forecasts for the same target. How is the information in these multiple nonlinear forecast systems best “combined”? Assuming stationarity, in the limit of a very large forecast–outcome archive, each model-based probability density function can be weighted to form a “multimodel forecast” that will, in expectation, provide at least as much information as the most informative single model forecast system. If one of the forecast systems yields a probability distribution that reflects the distribution from which the outcome will be drawn, Bayesian model averaging will identify this forecast system as the preferred system in the limit as the number of forecast–outcome pairs goes to infinity. In many applications, like those of seasonal weather forecasting, data are precious; the archive is often limited to fewer than 26 entries. In addition, no perfect model is in hand. It is shown that in this case forming a single “multimodel probabilistic forecast” can be expected to prove misleading. These issues are investigated in the surrogate model (here a forecast system) regime, where using probabilistic forecasts of a simple mathematical system allows many limiting behaviors of forecast systems to be quantified and compared with those under more realistic conditions.
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Eckel, F. Anthony, Mark S. Allen e Matthew C. Sittel. "Estimation of Ambiguity in Ensemble Forecasts". Weather and Forecasting 27, n. 1 (1 febbraio 2012): 50–69. http://dx.doi.org/10.1175/waf-d-11-00015.1.

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Abstract Ambiguity is uncertainty in the prediction of forecast uncertainty, or in the forecast probability of a specific event, associated with random error in an ensemble forecast probability density function. In ensemble forecasting ambiguity arises from finite sampling and deficient simulation of the various sources of forecast uncertainty. This study introduces two practical methods of estimating ambiguity and demonstrates them on 5-day, 2-m temperature forecasts from the Japan Meteorological Agency’s Ensemble Prediction System. The first method uses the error characteristics of the calibrated ensemble as well as the ensemble spread to predict likely errors in forecast probability. The second method applies bootstrap resampling on the ensemble members to produce multiple likely values of forecast probability. Both methods include forecast calibration since ambiguity results from random and not systematic errors, which must be removed to reveal the ambiguity. Additionally, use of a more robust calibration technique (improving beyond just correcting average errors) is shown to reduce ambiguity. Validation using a low-order dynamical system reveals that both estimation methods have deficiencies but exhibit some skill, making them candidates for application to decision making—the subject of a companion paper.
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4

Schmid, W., S. Mecklenburg e J. Joss. "Short-term risk forecasts of heavy rainfall". Water Science and Technology 45, n. 2 (1 gennaio 2002): 121–25. http://dx.doi.org/10.2166/wst.2002.0036.

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Methodologies for risk forecasts of severe weather hardly exist on the scale of nowcasting (0–3 hours). Here we discuss short-term risk forecasts of heavy precipitation associated with local thunderstorms. We use COTREC/RainCast: a procedure to extrapolate radar images into the near future. An error density function is defined using the estimated error of location of the extrapolated radar patterns. The radar forecast is folded (“smeared”) with the density function, leading to a probability distribution of radar intensities. An algorithm to convert the radar intensities into values of precipitation intensity provides the desired probability (or risk) of heavy rainfall at any position within the considered window in space and time. We discuss, as an example, a flood event from summer 2000.
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5

Liu, Liyang, Junji Wu e Shaoliang Meng. "A Statistical Model for Wind Power Forecast Error Based on Kernel Density Estimation". Open Electrical & Electronic Engineering Journal 8, n. 1 (31 dicembre 2014): 501–7. http://dx.doi.org/10.2174/1874129001408010501.

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Wind power has been developed rapidly as a clean energy in recent years. The forecast error of wind power, however, makes it difficult to use wind power effectively. In some former statistical models, the forecast error was usually assumed to be a Gaussian distribution, which had proven to be unreliable after a statistical analysis. In this paper, a more suitable probability density function for wind power forecast error based on kernel density estimation was proposed. The proposed model is a non-parametric statistical algorithm and can directly obtain the probability density function from the error data, which do not need to make any assumptions. This paper also presented an optimal bandwidth algorithm for kernel density estimation by using particle swarm optimization, and employed a Chi-squared test to validate the model. Compared with Gaussian distribution and Beta distribution, the mean squared error and Chi-squared test show that the proposed model is more effective and reliable.
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Thorarinsdottir, Thordis L., e Matthew S. Johnson. "Probabilistic Wind Gust Forecasting Using Nonhomogeneous Gaussian Regression". Monthly Weather Review 140, n. 3 (1 febbraio 2012): 889–97. http://dx.doi.org/10.1175/mwr-d-11-00075.1.

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Abstract A joint probabilistic forecasting framework is proposed for maximum wind speed, the probability of gust, and, conditional on gust being observed, the maximum gust speed in a setting where only the maximum wind speed forecast is available. The framework employs the nonhomogeneous Gaussian regression (NGR) statistical postprocessing method with appropriately truncated Gaussian predictive distributions. For wind speed, the distribution is truncated at zero, the location parameter is a linear function of the wind speed ensemble forecast, and the scale parameter is a linear function of the ensemble variance. The gust forecasts are derived from the wind speed forecast using a gust factor, and the predictive distribution for gust speed is truncated according to its definition. The framework is applied to 48-h-ahead forecasts of wind speed over the North American Pacific Northwest obtained from the University of Washington mesoscale ensemble. The resulting density forecasts for wind speed and gust speed are calibrated and sharp, and offer substantial improvement in predictive performance over the raw ensemble or climatological reference forecasts.
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Schuhen, Nina, Thordis L. Thorarinsdottir e Tilmann Gneiting. "Ensemble Model Output Statistics for Wind Vectors". Monthly Weather Review 140, n. 10 (1 ottobre 2012): 3204–19. http://dx.doi.org/10.1175/mwr-d-12-00028.1.

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Abstract A bivariate ensemble model output statistics (EMOS) technique for the postprocessing of ensemble forecasts of two-dimensional wind vectors is proposed, where the postprocessed probabilistic forecast takes the form of a bivariate normal probability density function. The postprocessed means and variances of the wind vector components are linearly bias-corrected versions of the ensemble means and ensemble variances, respectively, and the conditional correlation between the wind components is represented by a trigonometric function of the ensemble mean wind direction. In a case study on 48-h forecasts of wind vectors over the North American Pacific Northwest with the University of Washington Mesoscale Ensemble, the bivariate EMOS density forecasts were calibrated and sharp, and showed considerable improvement over the raw ensemble and reference forecasts, including ensemble copula coupling.
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Qi, Haixia, Xiefei Zhi, Tao Peng, Yongqing Bai e Chunze Lin. "Comparative Study on Probabilistic Forecasts of Heavy Rainfall in Mountainous Areas of the Wujiang River Basin in China Based on TIGGE Data". Atmosphere 10, n. 10 (9 ottobre 2019): 608. http://dx.doi.org/10.3390/atmos10100608.

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Based on the ensemble precipitation forecast data in the summers of 2014–2018 from the Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE), a comparative study of two multi-model ensemble methods, the Bayesian model average (BMA) and the logistic regression (LR), was conducted. Meanwhile, forecasts of heavy precipitation from the two models over the Wujiang River Basin in China for the summer of 2018 were compared to verify their performances. The training period sensitivity test results show that a training period of 2 years was the best for BMA probability forecast model. Compared with the BMA method, the LR model required more statistical samples and its optimal length of the training period was 5 years. According to the Brier score (BS), for precipitation events exceeding 10 mm with lead times of 1–7 days, the BMA outperformed the LR and the raw ensemble prediction system forecasts (RAW) except for forecasts with a lead time of 1 day. Furthermore, for heavy rainfall events exceeding 25 and 50 mm, the RAW and the BMA performed much the same in terms of prediction. The reliability diagram of the two multi-model ensembles (i.e., BMA and LR) was more reliable than the RAW for heavy and moderate rainfall forecasts, and the BMA model had the best performance. The BMA probabilistic forecast can produce a highly concentrated probability density function (PDF) curve and can also provide deterministic forecasts through analyzing percentile forecast results. With regard to the heavy rainfall forecast in mountainous areas, it is recommended to refer to the forecast with a larger percentile between the 75th and 90th percentiles. Nevertheless, extreme events with low probability forecasts may occur and cannot be ignored.
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Veenhuis, Bruce A. "Spread Calibration of Ensemble MOS Forecasts". Monthly Weather Review 141, n. 7 (1 luglio 2013): 2467–82. http://dx.doi.org/10.1175/mwr-d-12-00191.1.

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Abstract Ensemble forecasting systems often contain systematic biases and spread deficiencies that can be corrected by statistical postprocessing. This study presents an improvement to an ensemble statistical postprocessing technique, called ensemble kernel density model output statistics (EKDMOS). EKDMOS uses model output statistics (MOS) equations and spread–skill relationships to generate calibrated probabilistic forecasts. The MOS equations are multiple linear regression equations developed by relating observations to ensemble mean-based predictors. The spread–skill relationships are one-term linear regression equations that predict the expected accuracy of the ensemble mean given the ensemble spread. To generate an EKDMOS forecast, the MOS equations are applied to each ensemble member. Kernel density fitting is used to create a probability density function (PDF) from the ensemble MOS forecasts. The PDF spread is adjusted to match the spread predicted by the spread–skill relationship, producing a calibrated forecast. The improved EKDMOS technique was used to produce probabilistic 2-m temperature forecasts from the North American Ensemble Forecast System (NAEFS) over the period 1 October 2007–31 March 2010. The results were compared with an earlier spread adjustment technique, as well as forecasts generated by rank sorting the bias-corrected ensemble members. Compared to the other techniques, the new EKDMOS forecasts were more reliable, had a better calibrated spread–error relationship, and showed increased day-to-day spread variability.
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Rodríguez, Lissette Guzmán, Vagner Anabor, Franciano Scremin Puhales e Everson Dal Piva. "ESTIMATIVA DA PROBABILIDADE DE OCORRÊNCIA DE PRECIPITAÇÃO, A PARTIR DE TÉCNICAS ESTATÍSTICAS NÃO PARAMÉTRICAS APLICADAS A SIMULAÇÕES NUMÉRICAS DE WRF. UM CASO DE ESTUDO". Ciência e Natura 38 (20 luglio 2016): 491. http://dx.doi.org/10.5902/2179460x20193.

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In this paper was used the kernel density estimation (KDE), a nonparametric method to estimate the probability density function of a random variable, to obtain a probabilistic precipitation forecast, from an ensemble prediction with the WRF model. The nine members of the prediction were obtained by varying the convective parameterization of the model, for a heavy precipitation event in southern Brazil. Evaluating the results, the estimated probabilities obtained for periods of 3 and 24 hours, and various thresholds of precipitation, were compared with the estimated precipitation of the TRMM, without showing a clear morphological correspondence between them. For accumulated in 24 hours, it was possible to compare the specific values of the observations of INMET, finding better coherence between the observations and the predicted probabilities. Skill scores were calculated from contingency tables, for different ranks of probabilities, and the forecast of heavy rain had higher proportion correct in all ranks of probabilities, and forecasted precipitation with probability of 75%, for any threshold, did not produce false alarms. Furthermore, the precipitation of lower intensity with marginal probability was over-forecasted, showing also higher index of false alarms.
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Tesi sul tema "Forecast probability density function"

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Саркісян, Анна Оганесівна. "Методи і моделі прогнозування актуарних ризиків". Bachelor's thesis, КПІ ім. Ігоря Сікорського, 2021. https://ela.kpi.ua/handle/123456789/45229.

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Дипломна робота: 169 с., 21 табл., 48 рис., 2 дод., 29 джерел. Об’єкт дослідження – актуарні ризики, які досліджуються за допомогою байєсівської методології, як потужного інструментарію врахування апріорної та вибіркової інформації з метою уточнення закону розподілу випадкової величини, а також задача прогнозування величини страхових виплат як складна задача актуарної математики, що може бути розв’язана засобами байєсівської методології. Мета роботи – дослідження можливості застосування байєсівської методології для підвищення якості оцінок прогнозів можливих втрат в актуарних задачах; розгляд задач, розв’язок яких може бути покращено шляхом використання байєсівських методів, аналіз задачі прогнозування розподілу величини страхових виплат як такої, що може бути успішно розв’язана за допомогою байєсівських методів аналізу. Моделі - досліджувались байєсівські методи аналізу як інструмент врахування вибіркової та апріорної інформації і модифікації на її основі запропонованих моделей, актуарні ризики як перспективна область застосування байєсівських методів дослідження невизначеності, уточнення структури та покращення адекватності прогностичних якостей моделей. Отримані результати – побудована модель аналізу та прогнозування виплат страхової та модель кількості звернень до страхової. Прогнозні припущення щодо розвитку об’єкту дослідження – узагальнення запропонованого методу аналізу різних типів розподілів випадкових величин, що зустрічаються у страхуванні, проведення дослідження точності моделі залежно від вибору нормуючого коефіцієнта, модифікація відомих методів аналізу та управління страховими ризиками з використанням байєсівської методики.
Bachelor thesis: 169 p., 21 tabl., 48 fig., 2 append., 29 sources. Object of study - actuarial risks, which are studied using Bayesian methodology as a powerful tool for a priori and sample information to clarify the law of distribution of random variables, as well as the problem of predicting the amount of insurance benefits as a complex problem of actuarial mathematics that can be solved by means of Bayesian methodology. Purpose - to study the possibility of applying the Bayesian methodology to improve the quality of estimates of forecasts of possible losses in actuarial problems; consideration of problems, the solution of which can be improved by using Bayesian methods, analysis of the problem of forecasting the distribution of the amount of insurance benefits as such, which can be successfully solved using Bayesian methods of analysis. Used models - Bayesian methods of analysis were studied as a tool to take into account sample and a priori information and modify the proposed models based on it, actuarial risks as a promising area of application of Bayesian methods of uncertainty research, refinement of structure and improvement of adequacy of prognostic qualities of models. Results - a model of analysis and forecasting of insurance payments and a model of the number of appeals to the insurance company. Predictive assumptions about the development of the object of study - generalization of the proposed method of analysis of different types of distributions of random variables found in insurance, study the accuracy of the model depending on the choice of standardization, modification of known methods of analysis and management of insurance risks using Bayesian methodology.
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Pai, Madhusudan Gurpura. "Probability density function formalism for multiphase flows". [Ames, Iowa : Iowa State University], 2007.

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Aguirre-Saldivar, Rina Guadalupe. "Two scalar probability density function models for turbulent flames". Thesis, Imperial College London, 1987. http://hdl.handle.net/10044/1/38213.

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Joshi, Niranjan Bhaskar. "Non-parametric probability density function estimation for medical images". Thesis, University of Oxford, 2008. http://ora.ox.ac.uk/objects/uuid:ebc6af07-770b-4fee-9dc9-5ebbe452a0c1.

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Abstract (sommario):
The estimation of probability density functions (PDF) of intensity values plays an important role in medical image analysis. Non-parametric PDF estimation methods have the advantage of generality in their application. The two most popular estimators in image analysis methods to perform the non-parametric PDF estimation task are the histogram and the kernel density estimator. But these popular estimators crucially need to be ‘tuned’ by setting a number of parameters and may be either computationally inefficient or need a large amount of training data. In this thesis, we critically analyse and further develop a recently proposed non-parametric PDF estimation method for signals, called the NP windows method. We propose three new algorithms to compute PDF estimates using the NP windows method. One of these algorithms, called the log-basis algorithm, provides an easier and faster way to compute the NP windows estimate, and allows us to compare the NP windows method with the two existing popular estimators. Results show that the NP windows method is fast and can estimate PDFs with a significantly smaller amount of training data. Moreover, it does not require any additional parameter settings. To demonstrate utility of the NP windows method in image analysis we consider its application to image segmentation. To do this, we first describe the distribution of intensity values in the image with a mixture of non-parametric distributions. We estimate these distributions using the NP windows method. We then use this novel mixture model to evolve curves with the well-known level set framework for image segmentation. We also take into account the partial volume effect that assumes importance in medical image analysis methods. In the final part of the thesis, we apply our non-parametric mixture model (NPMM) based level set segmentation framework to segment colorectal MR images. The segmentation of colorectal MR images is made challenging due to sparsity and ambiguity of features, presence of various artifacts, and complex anatomy of the region. We propose to use the monogenic signal (local energy, phase, and orientation) to overcome the first difficulty, and the NPMM to overcome the remaining two. Results are improved substantially on those that have been reported previously. We also present various ways to visualise clinically useful information obtained with our segmentations in a 3-dimensional manner.
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Louloudi, Sofia. "Transported probability density function : modelling of turbulent jet flames". Thesis, Imperial College London, 2003. http://hdl.handle.net/10044/1/8007.

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Hulek, Tomas. "Modelling of turbulent combustion using transported probability density function methods". Thesis, Imperial College London, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339223.

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Rahikainen, I. (Ilkka). "Direct methodology for estimating the risk neutral probability density function". Master's thesis, University of Oulu, 2014. http://urn.fi/URN:NBN:fi:oulu-201404241289.

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The target of the study is to find out if the direct methodology could provide same information about the parameters of the risk neutral probability density function (RND) than the reference RND methodologies. The direct methodology is based on for defining the parameters of the RND from underlying asset by using futures contracts and only few at-the-money (ATM) and/or close at-the-money (ATM) options on asset. Of course for enabling the analysis of the feasibility of the direct methodology the reference RNDs must be estimated from the option data. Finally the results of estimating the parameters by the direct methodology are compared to the results of estimating the parameters by the selected reference methodologies for understanding if the direct methodology can be used for understanding the key parameters of the RND. The study is based on S&P 500 index option data from year 2008 for estimating the reference RNDs and for defining the reference moments from the reference RNDs. The S&P 500 futures contract data is necessary for finding the expectation value estimation for the direct methodology. Only few ATM and/or close ATM options from the S&P 500 index option data are necessary for getting the standard deviation estimation for the direct methodology. Both parametric and non-parametric methods were implemented for defining reference RNDs. The reference RND estimation results are presented so that the reference RND estimation methodologies can be compared to each other. The moments of the reference RNDs were calculated from the RND estimation results so that the moments of the direct methodology can be compared to the moments of the reference methodologies. The futures contracts are used in the direct methodology for getting the expectation value estimation of the RND. Only few ATM and/or close ATM options are used in the direct methodology for getting the standard deviation estimation of the RND. The implied volatility is calculated from option prices using ATM and/or close ATM options only. Based on implied volatility the standard deviation can be calculated directly using time scaling equations. Skewness and kurtosis can be calculated from the estimated expectation value and the estimated standard deviation by using the assumption of the lognormal distribution. Based on the results the direct methodology is acceptable for getting the expectation value estimation using the futures contract value directly instead of the expectation value, which is calculated from the RND of full option data, if and only if the time to maturity is relative short. The standard deviation estimation can be calculated from few ATM and/or at close ATM options instead of calculating the RND from full option data only if the time to maturity is relative short. Skewness and kurtosis were calculated from the expectation value estimation and the standard deviation estimation by using the assumption of the lognormal distribution. Skewness and kurtosis could not be estimated by using the assumption of the lognormal distribution because the lognormal distribution is not correct generic assumption for the RND distributions.
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Hao, Wei-Da. "Waveform Estimation with Jitter Noise by Pseudo Symmetrical Probability Density Function". PDXScholar, 1993. https://pdxscholar.library.pdx.edu/open_access_etds/4587.

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A new method for solving jitter noise in estimating high frequency waveform is proposed. It reduces the bias of the estimation in those points where all the other methods fail to achieve. It provides preliminary models for estimating percentiles in Normal, Exponential probability density function. Based on the model for Normal probability density function, a model for any probability density function is derived. The resulting percentiles, in turn, are used as estimates for the amplitude of the waveform. Simulation results show us with satisfactory accuracy.
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Weerasinghe, Weerasinghe Mudalige Sujith Rohitha. "Application of Lagrangian probability density function approach to turbulent reacting flows". Thesis, Imperial College London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.392476.

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Kakhi, M. "The transported probability density function approach for predicting turbulent combusting flows". Thesis, Imperial College London, 1994. http://hdl.handle.net/10044/1/8729.

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Libri sul tema "Forecast probability density function"

1

Churnside, James H. Probability density function of optical scintillations (scintillation distribution). Boulder, Colo: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, 1989.

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Yamazaki, Hidekatsu. Determination of wave height spectrum by means of a joint probability density function. College Station, Tex: Sea Grant College Program, Texas A & M University, 1985.

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Fornari, Fabio. Recovering the probability density function of asset prices using GARCH as diffusion approximations. [Roma]: Banca d'Italia, 2001.

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Fornari, Fabio. The probability density function of interest rates implied in the price of options. Rome: Banca d'Italia, 1998.

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Ma, Xiaofang. Computation of the probability density function and the cumulative distribution function of the generalized gamma variance model. Ottawa: National Library of Canada, 2002.

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Simon, M. Steady-state probability density function of the phase error for a DPLL with an integrate-and-dump device. Pasadena, Calif: National Aeronautics and Space Administration, Jet Propulsion Laboratory, California Institute of Technology, 1986.

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J, Lataitis R., e Wave Propagation Laboratory, a cura di. Probability density function of optical scintillations (scintillation distribution). Boulder, Colo: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, Wave Propagation Laboratory, 1989.

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Topcu, Mehmet. Measured probability density function of a phased-locked loop output. 1987.

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EUPDF, an Eulerian-based Monte Carlo probability density function (PDF) solver: User's manual. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1998.

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Center, Lewis Research, a cura di. EUPDF, an Eulerian-based Monte Carlo probability density function (PDF) solver: User's manual. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1998.

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Capitoli di libri sul tema "Forecast probability density function"

1

Gooch, Jan W. "Probability Density Function". In Encyclopedic Dictionary of Polymers, 992. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15330.

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Gooch, Jan W. "Probability Density Function". In Encyclopedic Dictionary of Polymers, 590. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_9466.

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Nascimento, Abraão D. C. "Probability Density Function". In Encyclopedia of Mathematical Geosciences, 1–5. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-26050-7_257-1.

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Dohmen, Jos J., Theo G. J. Beelen, Oryna Dvortsova, E. Jan W. ter Maten, Bratislav Tasić e Rick Janssen. "Calibration of Probability Density Function". In Mathematics in Industry, 401–24. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30726-4_18.

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Suciu, Nicolae. "Probability and Filtered Density Function Approaches". In Diffusion in Random Fields, 157–91. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15081-5_6.

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Gupta, Arjun K., Tamas Varga e Taras Bodnar. "Probability Density Function and Expected Values". In Elliptically Contoured Models in Statistics and Portfolio Theory, 59–102. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8154-6_3.

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Bodschwinna, Horst, e Jörg Seewig. "Surface Statistics and Probability Density Function". In Encyclopedia of Tribology, 3464–72. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-0-387-92897-5_304.

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Gupta, A. K., e T. Varga. "Probability Density Function and Expected Values". In Elliptically Contoured Models in Statistics, 80–129. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1646-6_3.

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Nakano, T., D. Fukayama, T. Gotoh e K. Yamamoto. "Probability Density Function of Longitudinal Velocity Increment". In IUTAM Symposium on Geometry and Statistics of Turbulence, 127–32. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9638-1_15.

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Bayer, Christian, Peter K. Friz e Peter Laurence. "On the Probability Density Function of Baskets". In Large Deviations and Asymptotic Methods in Finance, 449–72. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11605-1_16.

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Atti di convegni sul tema "Forecast probability density function"

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Li, Gong, Jing Shi e Junyi Zhou. "Short Term Wind Speed Forecasting Based on Bayesian Model Averaging Method". In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-13055.

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Wind energy has been the world’s fastest growing source of clean and renewable energy in the past decade. One of the fundamental difficulties faced by power system operators, however, is the unpredictability and variability of wind power generation, which is closely connected with the continuous fluctuations of the wind resource. Good short-term wind speed forecasting methods and techniques are urgently needed since it is important for wind energy conversion systems in terms of the relevant issues associated with the dynamic control of the wind turbine and the integration of wind energy into the power system. This paper proposes the application of Bayesian Model Averaging (BMA) method in combining the one-hour-ahead short-term wind speed forecasts from different statistical models. Based on the hourly wind speed observations from one representative site within North Dakota, four statistical models are built and the corresponding forecast time series are obtained. These data are then analyzed by using BMA method. The goodness-of-fit test results show that the BMA method is superior to its component models by providing a more reliable and accurate description of the total predictive uncertainty than the original elements, leading to a sharper probability density function for the probabilistic wind speed predictions.
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Lipowsky, Holger, Stephan Staudacher, Michael Bauer e Klaus-Juergen Schmidt. "Application of Bayesian Forecasting to Change Detection and Prognosis of Gas Turbine Performance". In ASME Turbo Expo 2009: Power for Land, Sea, and Air. ASMEDC, 2009. http://dx.doi.org/10.1115/gt2009-59447.

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This paper presents a novel technique for automatic change detection of the performance of gas turbines. In addition to change detection the proposed technique has the ability to perform a prognosis of measurement values. The proposed technique is deemed to be new in the field of gas turbine monitoring and forms the basic building block of a patent pending filed by the authors [1]. The technique used is called Bayesian Forecasting and is applied to Dynamic Linear Models (DLMs). The idea of Bayesian Forecasting is based on Bayes’ Theorem, which enables the calculation of conditional probabilities. In combination with DLMs (which break down the chronological sequence of the observed parameter into mathematical components like value, gradient, etc.) Bayesian Forecasting can be used to calculate probability density functions prior to the next observation, so called forecast distributions. The change detection is carried out by comparing the current model with an alternative model which mean value is shifted by a prescribed offset. If the forecast distribution of the alternative model better fits the actual observation, a potential change is detected. To determine whether the respective observation is a single outlier or the first observation of a significant change, a special logic is developed. Studies have shown that a confident change detection is possible for a change height of only 1.5 times the standard deviation of the observed signal. In terms of prognostic abilities the proposed technique not only estimates the point of time of a potential limit exceedance of respective parameters, but also calculates confidence bounds as well as probability density and cumulative distribution functions for the prognosis.
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Serpoushan, Nima, Mostafa Zeinoddini e Maziar Golestani. "An Ensemble Kalman Filter Data Assimilation Scheme for Modeling the Wave Climate in Persian Gulf". In ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/omae2013-10399.

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In recent years, application and evaluation of the efficiency of different data assimilation methods has been a subject of interest in both wave hindcasting and forecasting systems. The main goal of the current study is to assess the efficiency of an ensemble Kalman filter (EnKF) data assimilation scheme in improving the wave simulation results in Persian Gulf. The so called region plays an important role in the oil and gas industry due to its Geographical and Morphological location and housing a large number of offshore platforms. A third generation wave model, SWAN, was employed in order to simulate the wave fields in the region. The three hours updated ECMWF wind data were used as the main driving force. The OpenDA toolbox, especially developed for efficient data assimilation purposes, was employed to smooth the chaotic nature of the non-linear wave simulation scheme. The OpenDA utilizes a number of methods that are based on Kalman filter algorithm but do not require the amount of computation efforts that are incurred by the classical filter algorithm. The EnKF is a variant of Kalman filter, where probability density function of a model state is represented by an ensemble of the model state. Two sets of records for significant wave heights and peak wave periods were used in the analysis process with EnKF to estimate the error covariance matrix. At analysis time, the forecast error covariance was computed by using the model forecasts ensembles. In overall and for the wave climate modeling, the initial conditions of the numerical model were updated using the improved system state, up to the current computing time level. This is achieved by incorporating the previous measurements into the Kalman filter algorithm. The model was then run into the future, driven by the new improved state conditions. The statistical results and diagrams showed that applying EnKF scheme leads to a noticeable improvement in significant wave heights. However, the accuracy of this technique was subjected to the location and number of observation stations and also ensemble size. With larger ensembles, results of error covariance estimation are more accurate but there is a limitation due to execution time of process and efficiency of the computations.
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Todd, Michael, William Gregory, Christopher Key, Michael Yeager e Jordan Ye. "Composite Laminate Fatigue Damage Detection and Prognosis Using Embedded Fiber Bragg Gratings". In ASME 2018 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/smasis2018-8182.

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In many structural applications the use of composite material systems in both retrofit and new design modes has expanded greatly. The performance benefits from composites such as weight reduction with increased strength, corrosion resistance, and improved thermal and acoustic properties, are balanced by a host of failure modes whose genesis and progression are not yet well understood. As such, structural health monitoring (SHM) plays a key role for in-situ assessment for the purposes of performance/operations optimization, maintenance planning, and overall life cycle cost reduction. In this work, arrays of fiber Bragg grating optical strain sensors are attached to glass-epoxy solid laminate composite specimens that were subsequently subjected to specific levels of fully reversed cyclic loading. The fatigue loading was designed to impose strain levels in the panel that would induce damage to the laminate at varying numbers of cycles. The objectives of this test series were to assess the ability of the fiber Bragg grating sensors to detect fatigue damage (using previously developed SHM algorithms) and to establish a dataset for the development of a prognostic model to be applied to a random magnitude of fully reversed strain loading. The prognostic approach is rooted in the Failure Forecast Method, whereby the periodic feature rate-of-change was regressed against time to arrive at a failure estimate. An uncertainty model for the predictor was built so that a probability density function could be computed around the time-of-failure estimate, from which mean, median, and mode predictors were compared for robustness.
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Zhang, Jinfang, Ruoxuan Tian e Di Wu. "Predictive Function Control of Output Probability Density Function". In 2018 Chinese Automation Congress (CAC). IEEE, 2018. http://dx.doi.org/10.1109/cac.2018.8623134.

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Meyers, Ronald E. "Quantum probability density function (QPDF) method". In Optics & Photonics 2005, a cura di Ronald E. Meyers e Yanhua Shih. SPIE, 2005. http://dx.doi.org/10.1117/12.620152.

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Ayala-Ramirez, Victor, Raul Sanchez-yanez, Oscar Ibarra-manzano e Francisco Montecillo-puente. "Probability density function approximation using fuzzy rules". In 2006 Multiconference on Electronics and Photonics. IEEE, 2006. http://dx.doi.org/10.1109/mep.2006.335667.

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Popov, Ivan A., Nikolay V. Sidorovsky e Leonid M. Veselov. "Probability density function of non-Gaussian speckle". In Optoelectronic Science and Engineering '94: International Conference, a cura di Wang Da-Heng, Anna Consortini e James B. Breckinridge. SPIE, 1994. http://dx.doi.org/10.1117/12.182180.

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Markhvida, Igor V., e Ludmila V. Chvyaleva. "Probability density function of speckle intensity crossing". In SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, a cura di Dennis R. Pape. SPIE, 1994. http://dx.doi.org/10.1117/12.179116.

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Dimitrov, Nedialko B., e Valentin T. Jordanov. "Probability density function transformation using Seeded Localized Averaging". In 2011 2nd International Conference on Advancements in Nuclear Instrumentation, Measurement Methods and their Applications (ANIMMA). IEEE, 2011. http://dx.doi.org/10.1109/animma.2011.6172859.

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Rapporti di organizzazioni sul tema "Forecast probability density function"

1

Ide, Kayo. Predictability and Ensemble Forecast Skill Enhancement Based on the Probability Density Function Estimation. Fort Belvoir, VA: Defense Technical Information Center, gennaio 2005. http://dx.doi.org/10.21236/ada429618.

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Ide, Kayo. Predictability and Ensemble-Forecast Skill Enhancement Based on the Probability Density Function Estimation. Fort Belvoir, VA: Defense Technical Information Center, settembre 1999. http://dx.doi.org/10.21236/ada630373.

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Ide, Kayo. Predictability and Ensemble-Forecast Skill Enhancement Based on the Probability Density Function Estimation. Fort Belvoir, VA: Defense Technical Information Center, settembre 2000. http://dx.doi.org/10.21236/ada624633.

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Ide, Kayo. Predictability and Ensemble-Forecast Skill Enhancement Based on the Probability Density Function Estimation. Fort Belvoir, VA: Defense Technical Information Center, agosto 2001. http://dx.doi.org/10.21236/ada625720.

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Hao, Wei-Da. Waveform Estimation with Jitter Noise by Pseudo Symmetrical Probability Density Function. Portland State University Library, gennaio 2000. http://dx.doi.org/10.15760/etd.6471.

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Chow, Winston C. Analysis of the Probability Density Function of the Monopulse Ratio Radar Signal. Fort Belvoir, VA: Defense Technical Information Center, agosto 1996. http://dx.doi.org/10.21236/ada315600.

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DESJARDIN, PAUL E., MELVIN R. BAER, RAYMOND L. BELL e EUGENE S. HERTEL, JR. Towards Numerical Simulation of Shock Induced Combustion Using Probability Density Function Approaches. Office of Scientific and Technical Information (OSTI), luglio 2002. http://dx.doi.org/10.2172/801388.

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Smith, Donald L., Denise Neudecker e Roberto Capote Noy. Investigation of the Effects of Probability Density Function Kurtosis on Evaluated Data Results. Office of Scientific and Technical Information (OSTI), aprile 2018. http://dx.doi.org/10.2172/1434430.

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Nuttall, Albert H. Saddlepoint Approximations for the Combined Probability and Joint Probability Density Function of Selected Order Statistics and the Sum of the Remainder. Fort Belvoir, VA: Defense Technical Information Center, febbraio 2004. http://dx.doi.org/10.21236/ada421711.

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Nuttall, Albert H. Joint Probability Density Function of Selected Order Statistics and the Sum of the Remaining Random Variables. Fort Belvoir, VA: Defense Technical Information Center, gennaio 2002. http://dx.doi.org/10.21236/ada399298.

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