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The differences between the deterministic predictions of model (1), and the observations on the plots give observations on the random shocks ε t due to ice storms, wind, insect outbreaks, abnormal weather, etc.… that have affected forest growth during the observation period (Zhou and Buongiorno 2004).
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Pre-program questions on students' at-risk behaviours that frequently violated safety regulations were utilized by observers to give observation feedback.
Given observations of the form in (2), we are interested in detecting whether f ∗ ∈ D or f∗is anomalous.
Smoothing stands for the estimation of the distributions of the sequence of states (X k,…,X p ) given observations (Y0,…,Y ℓ ) with 0≤k≤p≤ℓ≤n.
The difference in the occurrence rate of these two topside morphologies emphasizes the current inability to predict which morphology will be present in any given observations.
Statistical inference for HMMs often requires to solve Bayesian filtering and smoothing problems, i.e., the computation of the posterior distributions of sequences of hidden states given observations.
A set of weighted particles are used to approximate a density function corresponding to the probability of the location of the target given observations.
Given observations X1,…,X n of an ARMA process, the AIC statistic is defined as: AIC = − 2 · ln ( L ) + 2 ( p + q + 1 ), (15).
Definition 3. Given observations and, drawn independently and identically distributed from and, respectively, the unbiased estimate of MMD is the one-sample - statistic: (3).
Given observations of presynaptic activity, we first consider the statistical problem of estimating presynaptic excitability.
Given observations of noisy presynaptic activity, our adaptation model estimates the excitability of the presynaptic neuron on each timescale using approximate Bayesian inference (an assumed density filter).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com