We can make use of a lot of conveniences in R to accomplish such a simulation. For example, we don’t have to worry about random number generation, we can simply use therexp()function for an M/M/1 queue. It may not be the fastest code on the planet but it is guaranteed to be r...
For this problem, we’re going to use R’s ppois function, which gives the cumulative probability or expected value of an event- essentially it is a maximum likelihood estimator. This is a digital version of the table of probabilities included as an appendix in your favorite statistics book. ...
Polyester was the predominant synthetic polymer in all samples (81%), followed by polyethylene (5%), and nylon (3%). Microplastics were typically of smaller size than nonsynthetic particles. As the identified microplastics can be inhaled, these results highlight the potential direct human exposure ...
Subsequently, for a target population, scCube randomly samples the same proportion of grids based on the proportion of this population in the generated data, and assigns all cells (or spots) which belongs to this population to the spatial positions in the sampled grids. This step repeats \(c...
The Rexp in R function generates values from the exponential distribution and return the results, similar to the dexp exponential function. The exponential density function, the dexp exponential function, and the rexpcumulative distribution functiontake two arguments: ...
samples from the distributionftruncated to\(D_0\). Behaviour in\(D_1, \ldots D_{n}\)The distributionmrestricted to\(D_i\)satisfies $$\begin{aligned} \frac{1}{m(D_i)}m(x)\mathbbm {1}_{D_i}(x) \le \frac{h_i \vert D_i \vert }{(a-1)m(D_i)} \frac{1}{\vert D...
Table 2 Samples of previous contributions showing effect of AI branches within energy systems Full size table There are more and more research examples that are strongly similar in terms of idea as well as motivations whereas different in the proposed methodology to achieve this idea (Garud et al...
While simulating from a mixture of standard densities is relatively straightforward, when the component densities are easily simulated, to the point that many simulation methods exploit an intermediary mixture construction to speed up the production of pseudo-random samples from more challenging distribution...
Machine learning (ML) models can simulate flood risk by identifying critical non-linear relationships between flood damage locations and flood risk factors (FRFs). To explore it, Tampa Bay, Florida, is selected as a test site. The study’s goal is to sim
point_estimate = FALSEand choose the number of samples of the parameters to draw. The distribution of the regression coefficients is then drawn by sampling from the multivariate normal distribution. Furthermore, the PSA is vectorized since the loops over the parameter samples are written in C++....