In this paper we will present and demonstrate aguila, a tool for interactive dynamic visual analysis of gridded data that come as spatial or spatio-temporal probability distribution functions. Probability distribution functions are analysed in their cumulative form, and we can choose to visualize exceedance probabilities given a threshold value, or its inverse, the quantile values. Threshold va...

The aim of seismic inversion is mapping all of the subsurface structures from seismic data. Due to the band-limited nature of the seismic data, it is difficult to find a unique solution for seismic inversion. Deterministic methods of seismic inversion are based on try and error techniques and provide a smooth map of elastic properties, while stochastic methods produce high-resolution maps of el...

1 Probability distribution functions Probability distribution functions assign probabilities to each of the possible outcomes of a random experiment. These distributions can be continuous (e.g. grading on a 4.0 scale), or discrete (e.g. grading with letter grades) (see Fig. ??). Figure 1: Terminology for continuous and discrete probability distributions. A probability distribution function spec...

1 Probability distribution functions Probability distribution functions assign probabilities to each of the possible outcomes of a random experiment. These distributions can be continuous (e.g. grading on a 4.0 scale), or discrete (e.g. grading with letter grades) (see Fig. 2). Figure 1: Terminology for continuous and discrete probability distributions. A probability distribution function speci...

1 Probability distribution functions Probability distribution functions assign probabilities to each of the possible outcomes of a random experiment. These distributions can be continuous (e.g. grading on a 4.0 scale), or discrete (e.g. grading with letter grades) (see Fig. 2). Figure 1: Terminology for continuous and discrete probability distributions. A probability distribution function speci...

Road traffic volumes in intercity roads are generally estimated by probability functions, statistical techniques or meta-heuristic approaches such as artificial neural networks. As the road traffic volumes depend on input variables and mainly road geometrical design, weather conditions, day or night time, weekend or national holidays and so on, these are also estimated by pattern recognition te...