Update README.md

Author: krzjoa

README.md

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 * [ChainerRL](https://github.com/chainer/chainerrl) - A deep reinforcement learning library built on top of Chainer.
 
 ## Probabilistic Methods
-* [pomegranate](https://github.com/jmschrei/pomegranate) - Probabilistic and graphical models for Python. <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
 * [pyro](https://github.com/uber/pyro) - A flexible, scalable deep probabilistic programming library built on PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
+* [pomegranate](https://github.com/jmschrei/pomegranate) - Probabilistic and graphical models for Python. <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
 * [ZhuSuan](http://zhusuan.readthedocs.io/en/latest/) - Bayesian Deep Learning. <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [PyMC](https://github.com/pymc-devs/pymc) - Bayesian Stochastic Modelling in Python.
-* [PyMC3](http://docs.pymc.io/) - Python package for Bayesian statistical modeling and Probabilistic Machine Learning. <img height="20" src="img/theano_big.png" alt="Theano compatible">
-* [sampled](https://github.com/ColCarroll/sampled) - Decorator for reusable models in PyMC3.
-* [Edward](http://edwardlib.org/) - A library for probabilistic modeling, inference, and criticism. <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [InferPy](https://github.com/PGM-Lab/InferPy) - Deep Probabilistic Modelling Made Easy. <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [GPflow](http://gpflow.readthedocs.io/en/latest/?badge=latest) - Gaussian processes in TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [PyStan](https://github.com/stan-dev/pystan) - Bayesian inference using the No-U-Turn sampler (Python interface).
 * [sklearn-bayes](https://github.com/AmazaspShumik/sklearn-bayes) - Python package for Bayesian Machine Learning with scikit-learn API. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [skggm](https://github.com/skggm/skggm) - Estimation of general graphical models. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [pgmpy](https://github.com/pgmpy/pgmpy) - A python library for working with Probabilistic Graphical Models.
 * [skpro](https://github.com/alan-turing-institute/skpro) - Supervised domain-agnostic prediction framework for probabilistic modelling by [The Alan Turing Institute](https://www.turing.ac.uk/). <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [Aboleth](https://github.com/data61/aboleth) - A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation. <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [PtStat](https://github.com/stepelu/ptstat) - Probabilistic Programming and Statistical Inference in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
 * [PyVarInf](https://github.com/ctallec/pyvarinf) - Bayesian Deep Learning methods with Variational Inference for PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
 * [emcee](https://github.com/dfm/emcee) - The Python ensemble sampling toolkit for affine-invariant MCMC.