Dev4.1 new chain#34
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Dev3.2.1.1 Update headers and add visualization of continuous element
Add DataReduction methods to reduce distribution
Dev3.2.1.1
Add file headers.
#493 Use util.TailCall to elimination recursion in eliminateInOrder
Class representing the internal variable in Chain factors, i.e. the pair of the Chain's parent and the Chain
during Factor sampling creation, leading to only a single sample taken. Forward sampling was setting the value of observed elements instead of generating them fresh, leading to poor sampling in MH.
Fixes for two small bugs.
Internal variables to Chains now store the actual pair of values instead of a meaningless integer
Now includes tests for evidence on MAP elements. Some of the algorithm parameters might need finer tuning to optimize for tests passing in a reasonable amount of time.
Mostly just more comments. I switched the order of parameters in the constructor because I thought it made more sense this way. Also upped the time on one of the tests that was consistently failing.
Normal proposals only apply to atomic elements.
Mostly explaining the subleties related to densities of randomness and value.
To avoid confusion with observing values of elements, particularly in ProvEvidenceMarginalMAP.
Only use normal proposal if a >= 1 or b >= 1.
These are just copied from ContinuousTest and applied to the new atomic elements in the normalproposals package. The tests pass, but they run about 4-5 times slower! We should evaluate performance more carefully before integrating into the library.
Clarified that the density function must be finite over the bounded range. Additionally, we no longer use normal proposals for Gamma variables with small shape parameter.
Before, we were creating an Apache Commons NormalDistribution each time we wanted to compute the proposal probability. This ended up being painfully slow because of overhead associated with instantiating a random generator for this purpose. We really just want the ability to compute density and cumulative probability for an arbitrary normal distribution. Utilities for this were added in the Normal object. Also added more tests.
Marginal MAP and normal proposals
Without this change, this method exclusively throws RuntimeException.
Tests for Gibbs sampling using new Chain
sampling during factored algorithms. Also updated particle generator with more informative variable names, deprecated old names, and updated related files.
necessitated removing the ValueMaker trait from Beta and Dirchilet, which is fine since they are currently not used.
1. Minor changes for the single chain factor method (moved factor creation to factory) 2. Removed some warnings 3. Moved Collapsed Gibbs test to experimental package 4. Updated tutorial
Changed warning in particle generator to be more informative when
Fixed a typing issue in ParImportance that caused slowdowns in tests.
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