The scope of this work has been to apply multidimensional data visualization techniques to the study of the ancient building decay events. With this aim we have created new visual tools based on the shape variation (glyph), that with more traditional techniques based on the color have been implemented in object-oriented software programs using the innovative and powerful visualization toolkit (VTK), a free C++ class library for visualization and 3D computer graphics. The data used are climatic, chemical and environmental measurements detected on/near the Roman theatre in the city of Aosta, acquired in the ambit of an Italian research project focused on the restoration of ancient monuments
We present some considerations on time-oscillatory phenomena in hydrodynamical models
for semiconductors and study the existence of periodic solutions. For the one-dimensional,
viscous, isentropic model, written in Lagrangian mass coordinates, we state a first existence
result and give a sketch of the proof.
bstract: When we use stochastic differential equations as models of financial data that appear as time series, we have to estimate the equation parameters. For complex models this is not straightforward. Approximate maximum likelihood methods are useful tools for this purpose. We suggest the following approach: The likelihood function given by the time series and the parameters is estimated for fixed values of the parameter vector. We apply a standard optimization method that repeatedly calls the estimation procedure, with different parameter vectors as arguments, until the optimization converges. The likelihood function is the product of the transition probability densities given by the data. By solving the Fokker-Planck equation associated with the stochastic differential equation, one can obtain these probability densities. Exact, analytical solutions to the Fokker-Planck equation can rarely be found. We therefore apply a path integral method to find approximate solutions. This path integral method is based on the fact that solutions to stochastic differential equations are Markov processes. The time intervals between all the pairs of consecutive data are split in smaller partitions, so that the Euler-Maruyama method is fairly accurate. For all the pairs of data, the total probability law is then applied recursively with the delta distribution given by the first data point as the initial density. The propagating density is represented numerically on a finite, adaptive grid, and the Euler-Maruyama method provides an approximation to the conditional probability density that appears in the total probability law. The method is tested on artificially generated time series with known parameter vectors. It is seen that it yields satisfying parameter estimates for different models in quite reasonable CPU-time, even with thousands of data. It also compares favorably with other methods.
parameter estimation
stochastic differential equations
path integration
finance
time series
