You can even embed your new language in another, well-known general-purpose language, letting you reuse the host language's syntax. Haskell is great for this sort of work.
This is entirely a question of semantics. And what do probabilistic semantics give you? They make writing complex models for data much more natural. These languages allow you to abstract away much of the incidental complexity of specifying these models, making the logic clearer. In turn, this simplifies reasoning about the problem and working on a solution.
More generally, this is the motivation behind most domain-specific languages. The core idea is to express yourself in a way that fits the domain you're working in. For machine-learning, you want to express your program in terms of probability distributions. For writing parsers, you want to express yourself in terms of CFGs. As people like to repeat, a program written more for people to read than computers to execute, and reading something in terms relevant to the domain is easier than trying to force the domain to fit an existing programming language and paradigm.
[1]: http://projects.csail.mit.edu/church/wiki/Mixture_models
You can even embed your new language in another, well-known general-purpose language, letting you reuse the host language's syntax. Haskell is great for this sort of work.
This is entirely a question of semantics. And what do probabilistic semantics give you? They make writing complex models for data much more natural. These languages allow you to abstract away much of the incidental complexity of specifying these models, making the logic clearer. In turn, this simplifies reasoning about the problem and working on a solution.
More generally, this is the motivation behind most domain-specific languages. The core idea is to express yourself in a way that fits the domain you're working in. For machine-learning, you want to express your program in terms of probability distributions. For writing parsers, you want to express yourself in terms of CFGs. As people like to repeat, a program written more for people to read than computers to execute, and reading something in terms relevant to the domain is easier than trying to force the domain to fit an existing programming language and paradigm.