With more deviation than is standard

In a recent blog (click here ) I illustrated how to conduct Bayesian inference by applying it to a toy fishing prediction problem. This blog post hopes to offer an improvement. For a full understanding you'll need to read the original post, although you can skip over the description of my fishing trip. There is revised code too for this blog, which runs in Matlab or Octave; you can get the code here . The earlier blog was intended to show how easy it is to execute this form of inference and to provide some example code. Although I said the model was not to be taken too seriously, there were some “ugly” features of the model which were very awkward and gave paradoxical results. The purpose of this blog is to provide a neater version of the model and so avoid the inherent contradictions of the previous version. In the model we tried to infer or predict the number of available fish, denoted v , in the swim which an angler is fishing. The “swim” is the part of the water in front of the

(Since the publication of this blog post, I have updated the toy model presented here with a new version that does not employ the poor choices of the model below. The revisions in the new model are described here .) Having completed my second book of 2023, the excellent " Bournville " (see  review here ), I was keen to catch fish species number 2. In fact, my blogging is lagging well behind both my fishing and reading. Here is  a list of the books I have read so far  this year for the challenge (in case anyone might be interested in a sneak preview of what book reviews are in store). The mild weather earlier in January 2023 had passed and rural Surrey was in the grip of a bitter cold spell, something that’s generally not conducive to catching fish. In fact, as mentioned in the  blog post  about the first species of the year (the zander), I carried out a scientific study a few years back which demonstrated that I caught fewer zander when the weather was colder!  However, days