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simulation_screen [2014/06/02 15:26]
erik
simulation_screen [2014/06/02 16:30]
erik
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 ====Introduction==== ====Introduction====
  
-The simulation page lets you run [[deterministic simulation|deterministic]],​ [[probabilistic|probability analysis]] (Monte Carlo) or [[sensitivity analysis]] simulations. The error section lists problems that must be solved before a simulation can be performed.+The simulation page lets you run [[deterministic simulation|deterministic]],​ [[probabilistic ​simulations|probabilistic]] (Monte Carlo) or [[sensitivity analysis]] simulations. The error section lists problems that must be solved before a simulation can be performed.
  
 ====Toolbar==== ====Toolbar====
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 ===General=== ===General===
  
- * **Number of simulations** - This setting specifies how many simulations will be performed during a probabilistic simulation. __Default value = 1000__+  ​* **Number of simulations** - This setting specifies how many simulations will be performed during a probabilistic simulation. __Default value = 1000__
  
- * **Seed** - Initialises the seed value for the random number generator. If you set the same seed, then the sequence will be the same each run.+  ​* **Seed** - Initialises the seed value for the random number generator. If you set the same seed, then the sequence will be the same each run.
  
- * **Sampling** - Specifies the scheme for generating parameter samples, either **Monte Carlo** (pseudo random numbers) or **[[wp>​Latin hypercube sampling]]**.+  ​* **Sampling** - Specifies the scheme for generating parameter samples, either **Monte Carlo** (pseudo random numbers) or **[[wp>​Latin hypercube sampling]]**.
  
 ===Parameters=== ===Parameters===
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 A probabilistic simulation starts with sampling values for all the probabilistic parameters, using either a (random) Monte Carlo scheme or Latin Hypercube sampling. ​ A probabilistic simulation starts with sampling values for all the probabilistic parameters, using either a (random) Monte Carlo scheme or Latin Hypercube sampling. ​
  
-After the values are sorted so that correlated parameter values are used in the same iteration. For example, consider the parameters //weight// and //height//. These parameters are log-normally distributed and are positively correlated, meaning that it is common for heavy humans to also be tall. By assigning a correlation of say 0.5, the likelyhood of the weight parameter being large in the same iteration as the height parameter is large will increase.+After the values are sorted so that correlated parameter values are used in the same iteration. ​ 
 + 
 +For example, consider the parameters //weight// and //height//. These parameters are log-normally distributed and are positively correlated, meaning that it is common for heavy humans to also be tall. By assigning a correlation of say 0.5, the likelyhood of the weight parameter being large in the same iteration as the height parameter is large will increase.
  
 To add correlations you must first select the **Enabled** button. You then click the **Add** button for each parameter pair, select the two parameters from the left and right lists and enter a correlation between the two.  To add correlations you must first select the **Enabled** button. You then click the **Add** button for each parameter pair, select the two parameters from the left and right lists and enter a correlation between the two. 
simulation_screen.txt · Last modified: 2014/06/25 11:15 (external edit)