By default, **kima** uses a set of priors for the model parameters
(see [below](#default-priors)).
If you don't set new ones in the `kima_setup.cpp` file, 
the default ones will be used and the model will run.

But sometimes (most times?) you will want to consider different priors
for some parameters.

To change specific priors, we just need to re-define some variables 
inside the `RVmodel` constructor.  
**The rule is:** priors for the planet orbital parameters
require the `conditional` object, those for other parameters don't.

For example, we can define two new priors

```c++
RVmodel::RVmodel():fix(true),npmax(1)
{
    // define new prior for the extra white noise parameter, s
    Jprior = make_prior<Uniform>(1., 20.); // m/s

    // get the conditional prior object
    auto conditional = planets.get_conditional_prior();
    // define new prior for the orbital period(s)
    conditional->Pprior = make_prior<Gaussian>(15, 0.1); // days
}
```

Note the small difference in syntax when defining the two priors.  

> **Important:**  
> The priors for the orbital parameters are *the same* for all planets. 
> This is intentional!  
> If you're looking for a workaround, 
> see [this page](https://github.com/j-faria/kima/wiki/Transiting-planet).

The arguments between `<` `>` in the calls to `make_prior` (called *template*
arguments) specify the prior distribution and the arguments inside parentheses
set the parameters of each distribution. **kima** (and DNest4) currently
implement the following distributions:

| distribution         | arguments            | link                                                                                       |
|----------------------|----------------------|--------------------------------------------------------------------------------------------|
| `Uniform`            | lower, upper         |                                                                                            |
| `LogUniform`         | lower, upper         |  [scipy](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.loguniform.html) |
| `ModifiedLogUniform` | knee, upper          |                                                                                            |
| `Rayleigh`           | scale                |                                                                                            |
| `Triangular`         | lower, center, upper |                                                                                            |
| `Laplace`            | center, width        | [wiki](https://en.wikipedia.org/wiki/Laplace_distribution)                                 |
| `Kumaraswamy`        | alpha, beta          | [wiki](https://en.wikipedia.org/wiki/Kumaraswamy_distribution)                             |
| `Gaussian`           | mean, stdev          |                                                                                            |
| `Exponential`        | mean                 |                                                                                            |
| `Cauchy`             | location, scale      | [wiki](https://en.wikipedia.org/wiki/Cauchy_distribution)                                  |
| `Fixed`              | value                |                                                                                            |



### Checking if the priors are correct

For any **kima** model, it's very easy to check
if the priors are set up correctly.  
In the `OPTIONS` file, set the maximum number of levels to 1,
and run the model as usual.
With this setting, DNest4 will sample directly from the priors.

The `kima-checkpriors` script is provided with the *pykima* package
to make histograms of the samples of each parameter.
Simply call it with the column of the parameter you want to check
(look inside `sample.txt` to see which one is which)

For example

    kima-checkpriors 1

might print

    Histogram of column 1: extra_sigma
    number of samples: 10000
    max value: 1999.744937
    min value: 0.000128

and display the histogram of the prior samples

<img src=https://image.ibb.co/bKtfEd/priorJ.png width="400">


### Default priors

```c++
// orbital periods and semi-amplitudes

// if hyperpriors=true
Pprior = make_shared<Laplace>(exp(log_muP), wP);
Kprior = make_shared<Exponential>(exp(log_muK));

// if hyperpriors=false
Pprior = make_shared<LogUniform>(1.0, 1e5);
Kprior = make_shared<ModifiedLogUniform>(1.0, 1e3);


// eccentricities
eprior = make_shared<Uniform>(0, 1);
    // another good option is
    // eprior = make_shared<Kumaraswamy>(0.867, 3.03);

// orbital phase
phiprior = make_shared<Uniform>(0, 2*M_PI);
// argument of periastron
wprior = make_shared<Uniform>(0, 2*M_PI);


// systemic velocity; m/s
Cprior = make_prior<Uniform>( data.get_RV_min(), data.get_RV_max() );
                          // so between the minimum and maximum RV

// additional white noise; m/s
Jprior = make_prior<ModifiedLogUniform>(
            min(1.0, 0.1*data.get_max_RV_span()), data.get_max_RV_span() );

// coefficients of the trend (if trend=true);
// units are m/s/day, m/s/day², m/s/day³
slope_prior = make_prior<Gaussian>( 0.0, pow(10, data.get_trend_magnitude(1)) );
quadr_prior = make_prior<Gaussian>( 0.0, pow(10, data.get_trend_magnitude(2)) );
cubic_prior = make_prior<Gaussian>( 0.0, pow(10, data.get_trend_magnitude(3)) );

// between-instrument offsets; m/s
offsets_prior = make_prior<Uniform>( -data.get_RV_span(), data.get_RV_span() );

// GP hyperparameters
log_eta1_prior = make_prior<Uniform>(-5, 5);
eta2_prior = make_prior<LogUniform>(1, 100);
eta3_prior = make_prior<Uniform>(10, 40);
log_eta4_prior = make_prior<Uniform>(-1, 1);

// degrees of freedom for Student-t likelihood
nu_prior = make_prior<LogUniform>(2, 1000);

// moving average parameters
sigmaMA_prior = make_prior<Uniform>(-1, 1);
tauMA_prior = make_prior<LogUniform>(1, 100);

// hyperparameters for orbital period and semi-amplitude hierachical priors
// only if hyperpriors=true
log_muP_prior = make_shared<TruncatedCauchy>(log(365), 1.0, log(365)-21, log(365)+21);
wP_prior = make_shared<Uniform>(0.1, 3.0);
log_muK_prior = make_shared<TruncatedCauchy>(0.0, 1.0, -21.0, 21.0);

// correlation coefficients with activity indicators
betaprior = make_prior<Gaussian>(0, 1);
```

In the example `kima/examples/default_priors`, **kima** will sample
from (some of) these default priors.