Sentence examples for probabilistic density function from inspiring English sources

From the probabilistic density function of the experimental data, a two factors Central Composite Design (CCD) experimental matrix based on strain and CNT weight fraction input with their corresponding stress distribution was established.

Then the pointwise parameter estimation and model verification are performed for each constituent of concrete, and the one-dimensional (1-D) probabilistic density function (PDF) of the random field is obtained based on the proposed maximum possibility criterion.

The idea of the probabilistic framework is to compute the conditional probabilistic density function (pdf) of an estimated position given the measured fingerprint pattern at the target point.

Earthquake probabilities in a certain time window, for example in the next 30 years, can be calculated by fitting inter-earthquake times with a probabilistic density function.

In [7, 8], a method of probabilistic analysis was proposed to directly calculate the probabilistic density function of critical eigenvalues of a large scale power system from the probabilistic density function of gird connected multiple sources of wind power generation to investigate the impact of stochastic uncertainty of grid-connected wind generation on power system small-signal stability [9].

The reliability of the source location is expressed by a spatial probabilistic density function (PDF) (Saccorotti et al., 1998; Saccorotti and Del Pezzo, 2000; La Rocca et al., 2004).

This situation can be overcome to some extent using probabilistic density functions [52].

PLF can yield the probabilistic density functions (PDFs) and cumulative distribution functions (CDFs) of bus voltages and branch flows.

Comparing to traditional Gram Charlier expansion based PLF (GC-PLF) calculation method, the proposed ME-PLF calculation algorithm can obtain more reliable and accurate probabilistic density functions (PDFs) of bus voltages and branch flows in various WT parameter scenarios.

P x) and P y) are the margin probabilistic density functions of x and y, respectively.

Ten thousand Monte Carlo simulations per aetiology were run, with key input values randomly drawn from probabilistic density functions in each iteration.