I have a set of values for a surface (in pixels) that becomes bigger over time (exponentially). The surface consists of cells that divide over time. After doing some modelling, I came up with the following formula: $$S(t)=S_{initial}2^{t/a_d},$$ where $a_d$ is the age at which the cell divides. $S_{initial}$ is known. I am trying to estimate $a_d$. I simply tried the $\chi^2$ test: # Range of ages of division. a_range = np.linspace(1, 500, 100) # Set up an empty vector …
I am working with data in the form of content that is being reviewed to decide if it violates the policy standards or not. In the review process there are several methods to increase the chances of making the correct decision. Content might be sent for multi-review in which case it is reviewed additional times to help get to a correct decision. We can also check the historical accuracy scores of the humans reviewing the content, higher accuracy scores indicate …
Imagine we have the following equation: y=xz. We have y but not other ones. Note that y is like a matrix and we could as many sample we want. It is the values obtained from sensors. This means it will be a m*n dataset where m represents number of samples and n represents the number of sensors. I am wondering whether we can use machine learning techniques (any method including GAN) to solve the equation and get at least one …
Problem Statement: I need to do some Structural Equation Modelling at work to get the main factors in a marketing survey data-set. There are no assumed equations to perform SEM on so what would be the best exploratory way to create those equations out of the data All variables are very highly correlated across all the variables so how can we deal with that Please let me know if I can help you with any supplements.