It can be fairly common to find missing values in a dataset. Having only a few missing values isn’t generally a problem and those records can be deleted listwise. In other words the entire record is simply removed from the analysis. The problem is even with a limited amount missing data, that can translate into […]
This is my second follow-up to my previous two posts which were about predicting NBA games with an algorithm, and my first update to the algorithm. The algorithm's record is now 3-1, as it correctly predicted Boston and Oklahoma City as winners of the...
This is my second follow-up to my previous two posts which were about predicting NBA games with an algorithm, and my first update to the algorithm. The algorithm’s record is now 3-1, as it correctly predicted Boston and Oklahoma City Continue reading →
This is my second follow-up to my previous two posts which were about predicting NBA games with an algorithm, and my first update to the algorithm. The algorithm's record is now 3-1, as it correctly predicted Boston and Oklahoma City as winners of their past games.Upcoming things to doSadly, I have been a bit busy, and I have not been able to do any work on the algorithm the past…
In the previous post I gave a quick introduction to the CRAN R package smfsb, and how it can be used for simulation of Markov processes determined by stochastic kinetic networks. In this post I’ll show how to use data and particle MCMC techniques in order to carry out Bayesian inference for the parameters of [...]
The second edition of my textbook, Stochastic Modelling for Systems Biology was published on 7th November, 2011. One of the new features introduced into the new edition is an R package called smfsb which contains all of the code examples discussed in the text, which allow modelling, simulation and inference for stochastic kinetic models. The [...]
