965 resultados para LOG-S DISTRIBUTIONS
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In the power market, electricity prices play an important role at the economic level. The behavior of a price trend usually known as a structural break may change over time in terms of its mean value, its volatility, or it may change for a period of time before reverting back to its original behavior or switching to another style of behavior, and the latter is typically termed a regime shift or regime switch. Our task in this thesis is to develop an electricity price time series model that captures fat tailed distributions which can explain this behavior and analyze it for better understanding. For NordPool data used, the obtained Markov Regime-Switching model operates on two regimes: regular and non-regular. Three criteria have been considered price difference criterion, capacity/flow difference criterion and spikes in Finland criterion. The suitability of GARCH modeling to simulate multi-regime modeling is also studied.
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RESUMO Objetivou-se neste estudo avaliar a eficiência da função log-logística com dois e três parâmetros, γ = dapmin e truncada à direita para a descrição da estrutura diamétrica de povoamentos equiâneos, bem como ajustar um modelo de distribuição diamétrica utilizando as funções. A modelagem realizada pela função log-logística foi comparada com a modelagem realizada com a função Weibull. Para isso, utilizaram-se dados de parcelas permanentes de clones de Eucalyptus, mensuradas em seis ocasiões. A aderência das funções aos dados foi comprovada pela aplicação do teste de Kolmogorov-Smirnov (K-S). Os valores médios da estatística do teste K-S foram 0,0901; 0,0997; 0,1297; e 0,0616 para a função com 2P, 3P, α = dapmin e truncada à direita, respectivamente. Para a função Weibull, obteve-se a média de 0,0638 para a estatística do teste K-S. A função log-logística de dois parâmetros pode ser utilizada na modelagem da estrutura diamétrica de povoamentos de eucalipto.
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An experimental investigation is performed in a turbulent flow in a seven wire-wrapped rod bundle, mounted in an open air facility. Static pressure distributions are measured on central and peripheral rods. By using a Preston tube, the wall shear stress profiles are experimentally obtained along the perimeter of the rods. The geometric parameters of the test section are P/D=1.20 and H/D=15. The measuring section is located at L/D=40 from the air inlet. It is observed that the dimensionless static pressure and wall shear stress profiles are nearly independent of the Reynolds number and strongly dependent of the wire-spacer position, with abrupt variations of the parameters in the neighborhood of the wires.
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(Diameter and height distributions in a gallery forest tree community and some of its main species in central Brazil over a six-year period (1985-1991)). The diameter and height structure were studied over six years in approximately 64 ha of the Gama gallery forest in Brasília, DF. Trees from 10 cm dbh were measured every three years from 1985 in 151 (10 x 20 m) permanent plots. Natural regeneration (individuals under 10 cm dbh) was measured in subplots within the 200 m² plots. Most individuals and species were under 45 cm diameter and 20 m high while the maximum diameter for individual species ranged from 30 to 95 cm. The diameter structure was typical of a mixed tropical forest with the number of individuals decreasing with increasing size classes and showing little change over the six years. The most abundant species occupy different positions in the canopy and have different size structures.
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We examined the relationships between environmental variations in lotic ecosystems with the seasonal dynamics of macroalgae communities at different spatial scales: drainage basin of two rivers (Rio das Pedras and Rio Marrecas), shading (open and shaded stream segments), mesohabitat (riffles and pools), and microhabitats. Data collections were made on a monthly basis between January and December/2007. A total of 16 taxa were encountered (13 species and 3 vegetative groups). All of the biotic parameters (richness, abundance, diversity, equitability, and dominance) were found to be highly variable at all of the spatial scales evaluated. On the other hand, abiotic variables demonstrated differences only at mesohabitat (in terms of current velocity) and shaded habitat (in terms of irradiance) scales. The seasonality of the macroalgae community structure was strongly influenced by microhabitat variables (current velocity, substrate H', and irradiance), demonstrating their importance over time and at different scales. Regional variables (temperature, oxygen saturation, specific conductance, pH, and turbidity) were found to have little influence on the temporal dynamics of the macroalgae communities evaluated.
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This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.
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Dedikaatio: Petrus Brahe.
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1929/01/01.
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1927.
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1925/01/01.
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1928/07/01.
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1933.
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1931/01/01.
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1932.
