Statistics Blog

Earlier
This is the 75th post to this blog. Every 25 posts I produce an index of posts so far for easy reference. If I make it to post 100 I’ll do something similar. 25. Catalogue of my first 25 blog posts 50. Index to first 50 posts 51. Calling Scala code from R using rscala 52. Calling R from Scala sbt projects using rscala 53. Data frames and tables in Scala 54. HOFs, closures, partial application and currying to solve the function environment problem in Scala 55. First steps with monads in Scala 56. A scalable particle filter in Scala 57. Working with SBML using Scala 58. Scala for Data [...]
Sat, Aug 10, 2019
Source: Darren JW
Introduction In the previous post I showed how to write your own general-purpose monadic probabilistic programming language from scratch in 50 lines of (Scala) code. That post is a pre-requisite for this one, so if you haven’t read it, go back and have a quick skim through it before proceeding. In that post I tried to keep everything as simple as possible, but at the expense of both elegance and efficiency. In this post I’ll address one problem with the implementation from that post – the memory (and computational) overhead associated with forming the Cartesian product of particle sets during monadic [...]
Sat, Aug 10, 2019
Source: Darren JW
Another fantasy series of the gritty type, maybe not up to the level of the first ground-breaking Abercrombie’s but definitely great!  With some reminiscence of Lawrence’s first series but with a better defined and more complex universe and a not so repulsive central character. Maybe even not repulsive at all when considered past and current actions as described from his perspective… “I’ve run the equations on it. It took me two days to plot them. Bear in mind that this is far, far beyond any light matrix that I’ve seen calculated before.” The whole book is indeed written from Captain Ryhalt‘s viewpoint. [...]
Fri, Aug 09, 2019
Source: Xian Blog
Background In May I attended a great workshop on advances and challenges in machine learning languages at the CMS in Cambridge. There was an a good mix of people from different disciplines, and a bit of a theme around probabilistic programming. The workshop schedule includes links to many of the presentations, and is generally worth browsing. In particular, it includes a link to the slides for my presentation on a compositional approach to scalable Bayesian computation and probabilistic programming. I’ve given a few talks on this kind of thing over the last couple of years, at Newcastle, at the Isaac Newton [...]
Wed, Aug 07, 2019
Source: Darren JW
Introduction There is a fairly large literature on reaction-diffusion modelling using partial differential equations (PDEs). There is also a fairly large literature on stochastic modelling of coupled chemical reactions, which account for the discreteness of reacting species at low concentrations. There is some literature on combining the two, to form stochastic reaction-diffusion systems, but much less. In this post we will look at one approach to the stochastic reaction-diffusion problem, based on an underlying stochastic process often described by the reaction diffusion master equation (RDME). We will start by generating exact realisations from this process using the spatial Gillespie algorithm, before switching [...]
Tue, Jan 22, 2019
Source: Darren JW
In the previous post I gave a very quick introduction to the smfsb R package. As mentioned in that post, although good for teaching and learning, R isn’t a great language for serious scientific computing or computational statistics. So for the publication of the third edition of my textbook, Stochastic modelling for systems biology, I have created a library in the Scala programming language replicating the functionality provided by the R package. Here I will give a very quick introduction to the scala-smfsb library. Some familiarity with both Scala and the smfsb R package will be helpful, but is not [...]
Thu, Jan 03, 2019
Source: Darren JW

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