Update README.md

README.md

@@ -19,7 +19,7 @@ ELFI - Engine for Likelihood-Free Inference
 
 <img src="https://cloud.githubusercontent.com/assets/1233418/20178983/6e22ee44-a75c-11e6-8345-5934b55b9dc6.png" width="15%" align="right"></img>
 
-ELFI is a statistical software package written in Python for Approximative Bayesian Computation ([ABC](https://en.wikipedia.org/wiki/Approximate_Bayesian_computation)), also known e.g. as likelihood-free inference, simulator-based inference, approximative Bayesian inference etc. This is useful, when the likelihood function is unknown or difficult to evaluate, but a generative simulator model exists.
+ELFI is a statistical software package written in Python for Approximative Bayesian Computation ([ABC](https://en.wikipedia.org/wiki/Approximate_Bayesian_computation)), also known as likelihood-free inference, simulator-based inference, approximative Bayesian inference etc. This is useful, when the likelihood function is unknown or difficult to evaluate, but a generative simulator model exists.
 
 The probabilistic inference model is defined as a directed acyclic graph, which allows for an intuitive means to describe inherent dependencies in the model. The inference pipeline is automatically parallelized with [Dask](https://dask.pydata.org), which scales well from a desktop up to a cluster environment. The package includes functionality for input/output operations and visualization.