This is the first document in a "quick tour" of the Baysig programming language.
The tour can be found in these sections:
Baysig is a probabilistic, spatiotemporal and purely functional programming language. That's a bit of a mouthful, so I'll define what I mean by those terms before we dive in:
Probabilistic: the primary goal of Baysig is to support Bayesian inference in complex models, in particular dynamical systems models of observed data. Probability distributions are first-class in Baysig, meaning they can be passed around and manipulated.
Spatiotemporal: Baysig supports spatially and temporally indexed data and differential equations. In particular, it seamlessly supports inference in continuous-time models based on discretely observed data.
Functional: all computations are defined by applying purely mathematical functions; that is functions that have no side-effects but are also first-class such that they can be passed as arguments to other functions or stored. Baysig is inspired by several functional programming languages, in particular the syntax of Haskell and the semantics of Standard ML.
This document will give a quick tour of the Baysig language, starting with the foundations, functions and data types, before moving on to probability distributions, Bayesian inference and dynamical systems. Towards the end, we explain how the interaction with Baysig constitutes an environment for reproducible research.
This tour is not a thorough introduction to Baysig. Rather, it aims to give a sense of how Baysig is different from other programming languages and what kinds of things you can do with it.
The core of Baysig is purely functional programming language, in which the central notion is of course the function. Functions are simple to define
f x = 2 * x + 1
f 5 ⇒ 11
Apart from defining and applying functions, we can also define new data types, like this
data Day = Mon | Tue | Wed | Thu | Fri | Sat | Sun
That defines a data type (Day) which has seven different constructors (Mon - Sun), each specifying a possible value of the type Day. I can define a new variable with one such value
myFavoriteDay = Sat
That variable has a type - Day. We can declare that
myFavoriteDay : Day
but there is no need to. If you omit the type declaration, the compiler will always be able to figure it out for itself and will tell you if there is an inconsistency in your code -- a type error.
A constructor can be used as a pattern in a function definition:
isWeekend Sat = True isWeekend Sun = True isWeekend _ = False
Here, isWeekend has type
Day -> Bool; that is, it is function that takes a
Day and returns a
Bool. The Bool data type is defined as:
data Bool = True | False
That's almost all of Baysig. But you are allowed to do things that are banned in boring programming languages. Arguments passed to functions can themselves be functions:
twice g x = g (g x)
Here, the function twice takes two arguments of which the first (
g) is a function.
twice f 1 ⇒ 7
You can check for yourself that this makes sense from the definition of
What is the type of twice? The function
g must return a value of the same type as its argument, otherwise we would not be able to apply it again. But within the definition of
twice, it doesn't matter what that type is. It works for any type. That is, if
t is a type, g must be of type
t -> t. The concrete type can be filled in later, when twice is called with a particular argument (e.g.
f). By convention, type variables, which can be filled in by concrete types, are denoted by lower-case letters. Thus the type of twice in Baysig is
twice : (t-> t) -> t -> t
When passing around functions, it is not always convenient to have to give them names (such as
f x = 2*x+1). After all, integers can be passed without naming them; for instance in
f (2+2), the expression
2+2 is not assigned a name. Likewise, expressions denoting functions can be constructed anonymously:
\x-> x+1 is read as "the function that takes an argument
x and returns
twice f 1 can be written
twice (\x->2*x+1) 1. This is occasionally useful.
The quick tour of Baysig continues in the document Baysig quick tour: Probability distributions.