edits twiecki suggested, and track_progress -> progressbar

Author: jsalvatier

examples/stochastic_volatility.ipynb

@@ -23,7 +23,7 @@
 # 
 # $$ log(\frac{y_i}{y_{i-1}}) \sim t(\nu, 0, exp(-2 s_i)) $$
 # 
-# Here, $y$ is the daily return series and $s$ is the latent volatility process.
+# Here, $y$ is the daily return series and $s$ is the latent log volatility process.
 
 # <markdowncell>
 
@@ -37,12 +37,13 @@
 
 n = 400
 returns = np.genfromtxt("data/SP500.csv")[-n:]
+returns[:5]
 
 # <markdowncell>
 
 # Specifying the model in pymc mirrors its statistical specification. 
 # 
-# However, it is easier to sample the scale of the volatility process innovations, $\sigma$, on a log scale, so we create it using `TransformedVar` and use `logtransform`. `TransformedVar` creates one variable in the transformed space and one in the normal space. The one in the transformed space (here $\text{log}(\sigma) $) is the one over which sampling will occur, and the one in the normal space is the one to use throughout the rest of the model.
+# However, it is easier to sample the scale of the log volatility process innovations, $\sigma$, on a log scale, so we create it using `TransformedVar` and use `logtransform`. `TransformedVar` creates one variable in the transformed space and one in the normal space. The one in the transformed space (here $\text{log}(\sigma) $) is the one over which sampling will occur, and the one in the normal space is the one to use throughout the rest of the model.
 # 
 # It takes a variable name, a distribution and a transformation to use.
 
@@ -85,7 +86,7 @@ def hessian(point, nusd):
 
 # For this model, the full maximum a posteriori (MAP) point is degenerate and has infinite density. However, if we fix `log_sigma` and `nu` it is no longer degenerate, so we find the MAP with respect to the volatility process, 's', keeping `log_sigma` and `nu` constant at their default values. 
 # 
-# We use [l_bfgs_b](http://en.wikipedia.org/wiki/Limited-memory_BFGS) because it is more efficient for high dimensional functions (`s` has n elements).
+# We use L-BFGS because it is more efficient for high dimensional functions (`s` has n elements).
 
 # <codecell>
 
@@ -113,7 +114,7 @@ def hessian(point, nusd):
 title(str(s))
 plot(trace[s][::10].T,'b', alpha=.03);
 xlabel('time')
-ylabel('volatility')
+ylabel('log volatility')
 
 #figsize(12,6)
 traceplot(trace, model.vars[:-1]);
@@ -122,5 +123,5 @@ def hessian(point, nusd):
 
 # ## References
 # 
-#     1. Hoffman & Gelman. (2011). The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. http://arxiv.org/abs/1111.4246 
+# 1. Hoffman & Gelman. (2011). [The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo](http://arxiv.org/abs/1111.4246). 
 

examples/stochastic_volatility.py

@@ -8,7 +8,7 @@
 
 __all__ = ['sample', 'psample']
 
-def sample(draws, step, start=None, trace=None, track_progress=True, model=None):
+def sample(draws, step, start=None, trace=None, progressbar=True, model=None):
     """
     Draw a number of samples using the given step method.
     Multiple step methods supported via compound step method
@@ -25,7 +25,7 @@ def sample(draws, step, start=None, trace=None, track_progress=True, model=None)
         Starting point in parameter space (Defaults to trace.point(-1))
     trace : NpTrace
         A trace of past values (defaults to None)
-    track_progress : bool
+    progressbar : bool
         Flag for progress bar
     model : Model (optional if in `with` context)
 
@@ -57,7 +57,7 @@ def sample(draws, step, start=None, trace=None, track_progress=True, model=None)
     for i in xrange(draws):
         point = step.step(point)
         trace = trace.record(point)
-        if track_progress:
+        if progressbar:
             progress.update(i)
 
     return trace

pymc/sample.py

@@ -22,8 +22,7 @@ PyMC is a python module for Bayesian statistical modeling and model fitting whic
 ## Installation 
 
 ```
-git clone -b pymc3 [email protected]:pymc-devs/pymc.git
-python pymc/setup.py install
+pip install git+https://github.com/pymc-devs/pymc@pymc3
 ```
 
 ### Optional