With the help of numericjs library and existing github code samples I tested Baum-Welch estimation of Hidden Markov Model (HMM) with multi-dimensional gaussian observations.

See the demo here :

https://mzaradzki.github.io/probabilistic-javascript/demos/hmm.html

Despite my rough knowledge of javascript it turned out to be very easy to put it together.

Previously to run such demo I would have relied on a server side code, definitely not as convenient w.r.t. hosting solutions.

]]>x=numpy.random.normal(size=1e6)

but also solves the problem of running out of memory, too, since there is no “for loop” involved which is slower and memory intensive

x = [numpy.random.normal() for i in range(10**7)]

]]>Thanks for providing more information on the differences between how R and JS generate their random numbers. BTW it looks like node.js has a Mersenne library.

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