Update docs/src/model_simulation/finite_state_projection_simulation.md

Co-authored-by: Sam Isaacson <[email protected]>

Author: TorkelE

docs/src/model_simulation/finite_state_projection_simulation.md

@@ -35,7 +35,9 @@ heatmap(osol(50.0); xguide = "Y", yguide = "X")
 ```
   \
 
-Previously, we have shown how [*stochastic chemical kinetics*](@ref math_models_in_catalyst_sck_jumps) describe how chemical reaction network models can be [exactly simulated](@ref simulation_intro_jumps) (using e.g. [Gillespie's algorithm](https://en.wikipedia.org/wiki/Gillespie_algorithm)). We also described how the [SDE](@ref math_models_in_catalyst_cle_sdes) and [ODE](@ref math_models_in_catalyst_rre_odes) approaches were approximations of these jump simulations, and only valid for large copy numbers. To gain a good understanding of the system's time development, we typically have to carry out a large number of jump simulations. An alternative approach, however, is to instead simulate the *full probability distribution of the system*. This corresponds to the distribution from which these jump simulations are drawn.
+As previously discussed, [*stochastic chemical kinetics*](@ref math_models_in_catalyst_sck_jumps) models are mathematically given by jump processes that capture the exact times at which individual reactions occur, and the exact (integer) amounts of each chemical species at a given time. They represent a more microscopic model than [Chemical Langevin SDE](@ref math_models_in_catalyst_cle_sdes) models and [Reaction Rate Equation ODE](@ref math_models_in_catalyst_rre_odes) models, which can be interpreted as approximations to stochastic chemical kinetics models in the large population limit.
+
+One can study the dynamics of stochastic chemical kinetics models by simulating the stochastic processes using Monte Carlo methods. For example, they can be [exactly sampled](@ref simulation_intro_jumps) using [Stochastic Simulation Algorithms](https://en.wikipedia.org/wiki/Gillespie_algorithm) (SSAs), which are also often referred to as Gillespie's method. To gain a good understanding of a system's dynamics, one typically has to carry out a large number of jump process simulations to minimize sampling error. To avoid such sampling error, an alternative approach is to solve ODEs for the *full probability distribution* that these processes have a given value at each time. Knowing this distribution, one can then calculate any statistic of interest that can be sampled via running many SSA simulations.
 
 [*The chemical master equation*](https://en.wikipedia.org/wiki/Master_equation) (CME) describes the time development of this probability distribution[^1], and is given by a (possibly infinite) coupled system of ODEs (with one ODE for each possible chemical state, i.e. number configuration, of the system). For a system with a single species $X$, the CME looks like
 ```math