simulation_screen

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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)