reduced sample count

Author: jsalvatier

examples/stochastic_volatility.ipynb

@@ -94,7 +94,7 @@ def hessian(point, nusd):
 
 # <markdowncell>
 
-# We do a short initial run to get near the right area, then start again using a new Hessian at the new starting point to get faster sampling due to better scaling.
+# We do a short initial run to get near the right area, then start again using a new Hessian at the new starting point to get faster sampling due to better scaling. We do a short run since this is an interactive example.
 
 # <codecell>
 
@@ -105,13 +105,13 @@ def hessian(point, nusd):
     # Start next run at the last sampled position.
     start2 = trace.point(-1)
     step = HamiltonianMC(model.vars, hessian(start2, 6), path_length = 4.)
-    trace = sample(8000, step, trace=trace) 
+    trace = sample(2000, step, trace=trace) 
 
 # <codecell>
 
 #figsize(12,6)
 title(str(s))
-plot(trace[s][::10].T,'b', alpha=.01);
+plot(trace[s][::10].T,'b', alpha=.03);
 xlabel('time')
 ylabel('volatility')
 

examples/stochastic_volatility.py

@@ -2,12 +2,39 @@
 
 [![Build Status](https://travis-ci.org/pymc-devs/pymc.png)](https://travis-ci.org/pymc-devs/pymc)
 
-PyMC is a python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems.
+PyMC is a python module for Bayesian statistical modeling and model fitting which focuses on advanced Markov chain Monte Carlo fitting algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.
+
+## Features 
+
+ * Powerful sampling algorithms such as [Hamiltonian Monte Carlo](http://en.wikipedia.org/wiki/Hybrid_Monte_Carlo)
+ * Intuitive model specification syntax, for example, `x ~ N(0,1)` translates to `x = Normal(0,1)`
+ * All [Theano](http://deeplearning.net/software/theano/) features 
+  * Numpy indexing and broadcasting
+  * Linear algebra operators
+  * Computation optimization and dynamic C compilation
+ * Simple extensibility
 
 ## Guided Examples
- * [Simple model](http://nbviewer.ipython.org/urls/raw.github.com/pymc-devs/pymc/pymc3/examples/tutorial.ipynb)
+ * [Tutorial model](http://nbviewer.ipython.org/urls/raw.github.com/pymc-devs/pymc/pymc3/examples/tutorial.ipynb)
  * More advanced [Stochastic Volatility model](http://nbviewer.ipython.org/urls/raw.github.com/pymc-devs/pymc/pymc3/examples/stochastic_volatility.ipynb)
 
-# Features 
- * Advanced sampling algorithms like Hamiltonian Monte Carlo
- * [Theano](http://deeplearning.net/software/theano/) based optimization and dynamic C code generation.
+## Installation 
+
+```
+git clone -b pymc3 [email protected]:pymc-devs/pymc.git
+python pymc/setup.py install
+```
+
+### Optional
+
+[`scikits.sparse`](https://github.com/njsmith/scikits-sparse) enables sparse scaling matrices which are useful for large problems.
+
+Ubuntu:
+
+```
+sudo apt-get install libsuitesparse-dev 
+pip install git+https://github.com/njsmith/scikits-sparse.git
+```
+
+## License 
+[Apache License, Version 2.0](https://github.com/pymc-devs/pymc/blob/pymc3/LICENSE)