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### Science

### Science

### Science

### Simulations

### Simulations

### Simulations

### Simulations

### Simulations

### Parameter estimation

##### Machine learning algorithms are very good at approximating very general functions. Sometimes we want to constrain some parameters (e.g. cosmological parameters) given a set of observations. For standard observables (e.g. galaxy clustering) the likelihood function behind the data is known. By sampling it, constraints on the parameters can be placed.

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##### It is also possible to use neural networks to approximate the function that relates an observable (with unknown likelihood) to the value of the cosmological parameters.

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##### We have used this method to constrain the value of the cosmological and astrophysical parameters from measurements of the star-formation rate density (SFRD) of the SIMBA simulations from z=0 to z=7.

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