commit 52566ae09ce6d177b9a291f557014800c4a01965
parent 5ba60758e429b385b252b461831eb6bc7fd30145
Author: Jared Tobin <jared@jtobin.ca>
Date: Sat, 30 Jun 2018 17:44:47 +1200
Clean up README.
* Point to the Haddocks for examples
* Add standard 'please contribute!' message
Diffstat:
| M | README.md | | | 41 | +++++++---------------------------------- |
1 file changed, 7 insertions(+), 34 deletions(-)
diff --git a/README.md b/README.md
@@ -13,7 +13,6 @@ This implementation is a thin layer over `mwc-random`, which handles RNG
state-passing automatically by using a `PrimMonad` like `IO` or `ST s` under
the hood.
-
Examples
--------
@@ -29,46 +28,21 @@ invariant:
beta 1 10 >>= binomial 10
* Use do-notation to build complex joint distributions from composable,
-local conditionals:
+ local conditionals:
hierarchicalModel = do
- [c, d, e, f] <- replicateM 4 $ uniformR (1, 10)
+ [c, d, e, f] <- replicateM 4 (uniformR (1, 10))
a <- gamma c d
b <- gamma e f
p <- beta a b
n <- uniformR (5, 10)
binomial n p
+Check out the haddock-generated docs on
+[Hackage](https://hackage.haskell.org/package/mwc-probability) for other
+examples.
+## Etc.
-Included probability distributions
--------------
-
-* Continuous
-
- * Uniform
- * Normal
- * Log-Normal
- * Exponential
- * Inverse Gaussian
- * Laplace
- * Gamma
- * Inverse Gamma
- * Weibull
- * Chi-squared
- * Beta
- * Student t
- * Pareto
- * Dirichlet process
- * Symmetric Dirichlet process
-
-* Discrete
+PRs and issues welcome.
- * Discrete uniform
- * Zipf-Mandelbrot
- * Categorical
- * Bernoulli
- * Binomial
- * Negative Binomial
- * Multinomial
- * Poisson
-\ No newline at end of file
