Fabulous adventures in coding

Last time on FAIC we implemented the Markov process distribution, which is a distribution over state sequences, where the initial state and each subsequent state is random. There are lots of applications of Markov processes; a silly one that I’ve … Continue reading →

So… I’ve got good news and bad news. The good news is: I’ve described an interface for discrete probability distributions and implemented several distributions. I’ve shown how projecting a distribution is logically equivalent to the LINQ Select operator. I’ve shown … Continue reading →

[Code for this episode is here.] Last time in this series I left you with several challenges for improving our DSL for imperative probabilistic workflows. But first, a puzzle: Question One: You are walking down the street when you see … Continue reading →

I’m continuing my efforts to port over and update my old blog content. The previous episode is here. We’re still in the first few weeks of me blogging; I was pumping out articles at a rate I now consider to … Continue reading →

Last time in this series I proposed a stripped-down DSL for probabilistic workflows. Today, let’s see how we could “lower” it to ordinary C# 7 code. I’ll assume of course that we have all of the types and extension methods that … Continue reading →

Thanks again to the good people at Microsoft who have kept my old blog alive for now; my plan is to port the articles from the old site over, and then they will redirect from the old URLs to the … Continue reading →

Without further ado, here’s my proposed stripped-down C# that could be a DSL for probabilistic workflows; as we’ll see, it is quite similar to both enumerator blocks from C# 2 and async/await from C# 5. (Code for this episode can … Continue reading →

I’ve got no code for you this time; instead here are some musings about language design informed by our discussion so far. One of the most important questions to ask when designing a language feature is: what should the balance … Continue reading →

Before that silly diversion I mentioned that we will be needing the empty distribution; today, we’ll implement it. It’s quite straightforward, as you’d expect. [Code for this episode is here.] public sealed class Empty<T> : IDiscreteDistribution<T> { public static readonly Empty<T> Distribution = new Empty<T>(); private Empty() { } public T Sample() => throw new Exception(“Cannot sample from empty distribution”); public IEnumerable<T> Support() => … Continue reading →

Before we get going on today’s episode of FAIC, you might want to refresh your memory of what an additive monad is; I wrote an episode of my monad series on this subject. Briefly, an additive monad is a monad … Continue reading →

[Code is here.] This series is getting quite long and we’re not done yet! This would be a good time to quickly review where we’re at: We’re representing a particular discrete probability distribution P(A) over a small number of members … Continue reading →

[Code is here.] Last time on FAIC we made a correct, efficient implementation of SelectMany to bind a likelihood function and projection onto a prior, and gave a simple example. I deliberately chose “weird” numbers for all the weights; let’s … Continue reading →

UPDATE 3: Rock stars Scott Hanselman and Dan Fernandez and their colleagues have gotten my MSDN blog back up, and will also restore the late cbrumme’s blog as well. Thank you both, and everyone else at what I can only assume … Continue reading →

[Code is here.] Last time on FAIC we achieved two major results in our effort to build better probability tools. First, we demonstrated that the SelectMany implementation which applies a likelihood function to a prior probability is the bind operation … Continue reading →

[Code is here.] Last time on FAIC we discovered the interesting fact that conditional probabilities can be represented as likelihood functions, and that applying a conditional probability to a prior probability looks suspiciously like SelectMany, which is usually the bind … Continue reading →

[Code is here.] We’re going to spend the next while in this series on expressing probabilities that have some sort of extra condition associated with them. We’ll start with a simple example: I want to roll a fair six-sided die, but … Continue reading →

[Code is here.] Last time on FAIC I sketched out the “alias method”, which enables us to implement sampling from a weighted discrete distribution in constant time. The implementation is slightly tricky, but we’ll go through it carefully. The idea … Continue reading →

[Code is here.] Last time on FAIC we sketched out an O(log n) algorithm for sampling from a weighted distribution of integers by implementing a discrete variation on the “inverse transform” method where the “inverse” step involves a binary search. … Continue reading →

[Code is here.] Last time on FAIC I showed how we could implement more of the standard, straightforward probability distributions, like the Bernoulli distribution; the episode before I showed the standard discrete uniform distribution, also known as “roll an n-sided … Continue reading →

[Code is here.] Last time on FAIC I implemented our first discrete distribution, the “choose an integer between min and max” distribution. I thought I might knock out a couple more of the easy discrete distributions. The easiest discrete distribution … Continue reading →

We briefly interrupt my ongoing exploration of stochastic programming to point you at a new blog about pragmatic programming language design. My friend and erstwhile Roslyn colleague Anthony is once more living in Chicago and has just started blogging anew, … Continue reading →

[Code is here.] Last time on FAIC I showed how we could use a simple interface to cleanly implement concepts like “normal distribution” or “standard uniform distribution” in a natural style using fluent programming. For the next many episodes we’ll … Continue reading →

[Code is here.] Last time on FAIC I described a first attempt of how I’d like to fix System.Random: Make every method static and threadsafe Draw a clear distinction between crypto-strength and pseudo-random methods There were lots of good comments … Continue reading →

[Code is here.] Last time on FAIC I complained bitterly about the shortcomings of System.Random. In this episode, I’ll take a stab at the sort of thing I’d like to see in a redesigned random library. This is the “fix … Continue reading →

[Code is here.] How to say this delicately? I really, really dislike System.Random. Like, with a passion. It is so… awful. The C# design team tries hard to make the language a “pit of success”, where the natural way to … Continue reading →

This is the story of how I was no-hired by Microsoft; surprisingly, it is germane to our discussion of unusual list structures. I was a Waterloo co-op intern three times at Microsoft, each time working for the Visual Basic team; … Continue reading →

Last time on FAIC I gave a standard implementation of a persistent immutable linked list, with cheap — O(1) — Push, Pop, Peek and IsEmpty operations, and expensive — O(n) — Append and Concatenate operations. In this episode we’re going … Continue reading →

As long-time readers of my blog know, I’m a big fan of functional persistent immutable list data structures. I recently learned about a somewhat bizarre but very practical implementation of such lists, and naturally I wanted to implement it myself … Continue reading →

Pop quiz: Consider these little methods. (They could be static, inline, whatever, that doesn’t matter.) int[] A1() { Console.WriteLine(“A”); return new int[10]; } int[] A2() { Console.WriteLine(“A”); return null; } int B1() { Console.WriteLine(“B”); return 1; } int B2() { Console.WriteLine(“B”); return 11; } int C() { Console.WriteLine(“C”); … Continue reading →

I’ve recently been looking into a fascinating corner of mathematics that at first glance appears a little bit silly, but actually has far-reaching applications, from physics to numerical methods to machine learning. I thought I’d share what I’ve learned over … Continue reading →