v0.1.0, 4/28/2014 -- Initial commit

v0.1.1, 4/28/2014 -- Documentation and examples provided

v0.1.2, 5/5/2014 -- Changed instances of dtype=numpy.int to 
	dtype=numpy.int32, as other int types are not properly 
	recognized as integers on Unix machines, but as shorts or longs.

v0.1.3, 5/8/2014 -- Added optional removal of orphan states and chains
	in the bake method. Added two underflow protections: row
	normalization in the forward/backward algorithms, and a better
	logsumexp function. Fixed writing and reading so that the
	original distributions work. Still need to fix the functions
	under InverseGamma. Added more documentation.

v0.1.4, 5/11/2014 -- Infinite models are now possible, by simply not adding a transition to the end state. All algorithms accept this. You can make sure your model is an infinite model by calling "Model.is_infinite()". 

Added maximum a posteriori decoding, via "Model.maximum_a_posteriori( sequence )". This returns the mode of the posterior distribution of the states. This sequence may not necessarily be a valid sequence.

Added sampling from infinite models. If your model is infinite, you can pass in length to sample to get a sample of a certain length. You can also pass "path=True" to get the path of hidden states that generated that sample.