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RELEASE-NOTES.md

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- New benchmark suite added (see http://pandas.pydata.org/speed/pymc3/)
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- Generalized random seed types
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- Update loo, new improved algorithm (#2730)
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- New CSG (Constant Stochastic Gradient) approximate posterior sampling
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algorithm (#2544)
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- New CSG (Constant Stochastic Gradient) approximate posterior sampling algorithm (#2544)
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- Michael Osthege added support for population-samplers and implemented differential evolution metropolis (`DEMetropolis`). For models with correlated dimensions that can not use gradient-based samplers, the `DEMetropolis` sampler can give higher effective sampling rates. (also see [PR#2735](https://github.com/pymc-devs/pymc3/pull/2735))
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- Forestplot supports multiple traces (#2736)
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- Add new plot, densityplot (#2741)
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- DIC and BPIC calculations have been deprecated
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- Refactor HMC and implemented new warning system (#2677, #2808)
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### Fixes
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- Add Bayesian R2 score (for GLMs) `stats.r2_score` (#2696) and test (#2729).
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- SMC works with transformed variables (#2755)
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- Speedup OPVI (#2759)
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- Multiple minor fixes and improvements in the docs (#2775, #2786, #2787, #2789, #2790, #2794, #2799, #2809)
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### Deprecations
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