Update README.md (#99)

* Update README.md

* Update Project.toml

Author: yebai

Project.toml

@@ -26,6 +26,7 @@ Random = "1.6"
 Random123 = "1.3"
 Requires = "1.0"
 StatsFuns = "0.9, 1"
+SSMProblems = "0.1"
 julia = "1.6"
 
 [extras]

README.md

@@ -6,18 +6,9 @@
 [![Coverage](https://codecov.io/gh/TuringLang/AdvancedPS.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/TuringLang/AdvancedPS.jl)
 [![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle)
 
-AdvancedPS provides an efficient implementation of common particle based Monte Carlo samplers using the [AbstractMCMC](https://github.com/TuringLang/AbstractMCMC.jl) interface.
+AdvancedPS provides an efficient implementation of common particle-based Monte Carlo samplers using the [AbstractMCMC](https://github.com/TuringLang/AbstractMCMC.jl) interface.
 The package also relies on [Libtask](https://github.com/TuringLang/Libtask.jl) for task manipulation.
-AdvancedPS is part of the [Turing](https://turing.ml/stable/) ecosystem.
-
-### Installation
-
-Inside the Julia REPL
-```julia
-julia>] add AdvancedPS
-```
-
-### Examples
+AdvancedPS is part of the [Turing](https://turinglang.org/stable/) ecosystem.
 
 Detailed examples are available in the [documentation](https://turinglang.github.io/AdvancedPS.jl/dev/)
 
@@ -27,11 +18,11 @@ Detailed examples are available in the [documentation](https://turinglang.github
 
 2. Andrieu, Christophe, Arnaud Doucet, and Roman Holenstein. "Particle Markov chain Monte Carlo methods." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72, no. 3 (2010): 269-342.
 
-3. Tripuraneni, Nilesh, Shixiang Shane Gu, Hong Ge, and Zoubin Ghahramani. "Particle gibbs for infinite hidden Markov models." In Advances in Neural Information Processing Systems, pp. 2395-2403. 2015.
+3. Tripuraneni, Nilesh, Shixiang Shane Gu, Hong Ge, and Zoubin Ghahramani. "Particle Gibbs for infinite hidden Markov models." In Advances in Neural Information Processing Systems, pp. 2395-2403. 2015.
 
 4. Lindsten, Fredrik, Michael I. Jordan, and Thomas B. Schön. "Particle Gibbs with ancestor sampling." The Journal of Machine Learning Research 15, no. 1 (2014): 2145-2184.
 
-5. Pitt, Michael K., and Neil Shephard. "Filtering via simulation: Auxiliary particle filters." Journal of the American statistical association 94, no. 446 (1999): 590-599.
+5. Pitt, Michael K., and Neil Shephard. "Filtering via simulation: Auxiliary particle filters." Journal of the American Statistical Association 94, no. 446 (1999): 590-599.
 
 6. Doucet, Arnaud, Nando de Freitas, and Neil Gordon. "Sequential Monte Carlo Methods in Practice."