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Our main examples are finite quadratic variation processes with stationary increments and the bifractional Brownian motion.
On the contrary, for finite variation processes the sum of all the small positive and negative jumps are separately finite, in any interval.
Statistical process control (SPC) charts were produced to evaluate compliance data at the unit level over time, highlighting stable and predictable (common cause variation) or unstable and unpredictable (special cause variation) processes [21].
If the statistics of packet arrival and channel variation processes change, we need to determine the new MDP model by calculating all values of the state transition probability P x x ′ a before running DP or MPI.
Based on the properties of the quadratic variation and multipower variation processes, the structural dynamic equation is decomposed into the Gaussian continuous-time autoregressive (CAR) equation and the pure jump-driven CAR equation.
In contrast to classical assembling techniques for high variation processes, the novel approach presented here, based on the Statistical Feed-Forward Control Model (SFFCM), takes into account the dynamic evolution of the variation over time.
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The variation process depends on continual variation of a constant factor, such as a theme or a group of harmonies.
where the square brackets denote the quadratic variation process.
The quadratic variation process 〈W〉 t is also a continuous process with independent and stationary increments.
Its quadratic variation process is also a continuous process with independent and stationary increments.
By 〈X,X〉, we denote the square variation process of a continuous semimartingale X.
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