The phrase "accumulated probability distribution" is correct and usable in written English.
It can be used in contexts related to statistics, probability theory, or data analysis when discussing the distribution of probabilities that have been summed up over a range of values.
Example: "The accumulated probability distribution allows us to understand the likelihood of various outcomes occurring over time."
Alternatives: "cumulative probability distribution" or "aggregated probability distribution".
Emergency risk should be over 90% of accumulated probability.
Head bending is the accumulated probability for any larva to be bend at a specific angle in any frame.
The distribution of VTA spike-associated replay content was derived by accumulating the spatial probability distribution functions for the subset of 25 ms replay time bins that preceded VTA unit spikes by this fixed delay.
Our simulation studies indicate that the stable model estimates ancestral states with reduced error in comparison to the homogenous Ornstein-Uhlenbeck model, when traits evolve by accumulating increments from a probability distribution without fixed variance (Table 3).
Distributions of replayed spatial locations were derived by accumulating the spatial posterior probability distribution function across all 25 ms time bins of all replay events, in each recording, for SPW-Rs that occurred while the rat paused at forced reward locations.
We describe and justify a full-angle scattering (FAS) method to faithfully reproduce the accumulated differential angular Rutherford scattering probability distribution function (pdf) of particles in a plasma.
The risk profile for a specified category of adverse events and corresponding to a given operation time is defined as the complementary probability distribution function for the accumulated loss during the operation time.
Under the standard Brownian motion model of evolution, the continuous character evolves by accumulating random independent increments drawn from a probability distribution with constant mean zero and constant finite variance σ.
Stochastic process models of evolution regard an evolving trait as accumulating, over time, random increments drawn from some underlying probability distribution.
