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Author: marscher

notebooks/05-pcca-tpt.ipynb

@@ -9,7 +9,7 @@
     "<a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\"><img alt=\"Creative Commons Licence\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by/4.0/88x31.png\" title='This work is licensed under a Creative Commons Attribution 4.0 International License.' align=\"right\"/></a>\n",
     "\n",
     "In this notebook, we will cover how to use PCCA++ to extract a coarse representation of the MSM. We will further investigate how to use transition path theory (TPT) to follow the pathways of the processes.\n",
-    "When we want to analyze pathways, models with fewer states are ofter desirable, since these are easier to understand. PCCA allows us to assign the microstates directly to metastable macrostates and TPT uses this group assignment to compute fluxes and pathways. \n",
+    "When we want to analyze pathways, models with fewer states are more often desirable, since these are easier to understand. PCCA allows us to assign the microstates directly to **metastable** macrostates and TPT uses this group assignment to compute fluxes and pathways. \n",
     "\n",
     "\n",
     "Another method to get a model with viewer states are hidden Markov state models (HMM), introduced in notebook 07 [➜ 📓](07-hidden-markov-state-models.ipynb). In contrast to computing memberships of microstates to meta stable sets as in PCCA, in HMMs we directly obtain a model with viewer states.\n",

notebooks/06-expectations-and-observables.ipynb

@@ -55,7 +55,7 @@
    "source": [
     "## Case 1: preprocessed, two-dimensional data (toy model)\n",
     "\n",
-    "Again we estimate the MSM for the two-dimensional toy model to perform our further analysis on."
+    "Again we estimate the MSM for the two-dimensional toy model to perform further analysis on."
    ]
   },
   {