# Likelihood¶

`bilby`

likelihood objects are used in calculating the likelihood of the data
for some specific set of parameters. In mathematical notation, the likelihood
can be generically written as \(\mathcal{L}(d| \theta)\). How this is
coded up will depend on the problem, but `bilby`

expects all likelihood
objects to have a parameters attribute (a dictionary of key-value pairs) and
a log_likelihood() method. In this page, we’ll discuss how to write your own
Likelihood, and the standard likelihoods in `bilby`

.

## The simplest likelihood¶

To start with let’s consider perhaps the simplest likelihood we could write down, namely a Gaussian likelihood for a set of data \(\vec{x}=[x_1, x_2, \ldots, x_N]\). The likelihood for a single data point, given the mean \(\mu\) and standard-deviation \(\sigma\) is given by

Then, the likelihood for all \(N\) data points is

In practise, we implement the log-likelihood to avoid numerical overflow
errors. To code this up in `bilby`

, we would write a class like this:

```
class SimpleGaussianLikelihood(bilby.Likelihood):
def __init__(self, data):
"""
A very simple Gaussian likelihood
Parameters
----------
data: array_like
The data to analyse
"""
super().__init__(parameters={'mu': None, 'sigma': None})
self.data = data
self.N = len(data)
def log_likelihood(self):
mu = self.parameters['mu']
sigma = self.parameters['sigma']
res = self.data - mu
return -0.5 * (np.sum((res / sigma)**2)
+ self.N*np.log(2*np.pi*sigma**2))
```

This demonstrates the two required features of a `bilby`

`Likelihood`

object:

It has a parameters attribute (a dictionary with keys for all the parameters, in this case, initialised to

`None`

)It has a

`log_likelihood`

method which, when called returns the log likelihood for all the data.

You can find an example that uses this likelihood here.

Tip

Note that the example above subclasses the `bilby.Likelihood`

base
class, this simply provides a few in built functions. We recommend you also
do this when writing your own likelihood.

## General likelihood for fitting a function \(y(x)\) to some data with known noise¶

The previous example was rather simplistic, Let’s now consider that we have some dependent data \(\vec{y}=y_1, y_2, \ldots y_N\) measured at \(\vec{x}=x_1, x_2, \ldots, x_N\). We believe that the data is generated by additive Gaussian noise with a known variance \(\sigma^2\) and a function \(y(x; \theta)\) where \(\theta\) are some unknown parameters; that is

where \(n_i\) is drawn from a normal distribution with zero mean and standard deviation \(\sigma\). As such, \(y_i - y(x_i; \theta)\) itself will have a likelihood

As with the previous case, the likelihood for all the data is the product over the likelihood for each data point.

In `bilby`

, we can code this up as a likelihood in the following way:

```
class GaussianLikelihoodKnownNoise(bilby.Likelihood):
def __init__(self, x, y, sigma, function):
"""
A general Gaussian likelihood - the parameters are inferred from the
arguments of function
Parameters
----------
x, y: array_like
The data to analyse
sigma: float
The standard deviation of the noise
function:
The python function to fit to the data. Note, this must take the
dependent variable as its first argument. The other arguments are
will require a prior and will be sampled over (unless a fixed
value is given).
"""
self.x = x
self.y = y
self.sigma = sigma
self.N = len(x)
self.function = function
# These lines of code infer the parameters from the provided function
super().__init__(parameters=dict())
def log_likelihood(self):
res = self.y - self.function(self.x, **self.parameters)
return -0.5 * (np.sum((res / self.sigma)**2)
+ self.N*np.log(2*np.pi*self.sigma**2))
```

This likelihood can be given any python function, the data (in the form of
`x`

and `y`

) and the standard deviation of the noise. The
parameters are inferred from the arguments to the `function`

argument,
for example if, when instantiating the likelihood you passed in the following
function:

```
def f(x, a, b):
return x**2 + b
```

Then you would also need to provide priors for `a`

and `b`

. For
this likelihood, the first argument to `function`

is always assumed to
be the dependent variable.

Note

Here we have explicitly defined the `noise_log_likelihood`

method
as the case when there is no signal (i.e., \(y(x; \theta)=0\)).

You can see an example of this likelihood in the linear regression example.

## General likelihood for fitting a function \(y(x)\) to some data with unknown noise¶

In the last example, we considered only cases with known noise (e.g., a prespecified standard deviation. We now present a general function which can handle unknown noise (in which case you need to specify a prior for \(\sigma\), or known noise (in which case you pass the known noise in when instantiating the likelihood:

```
class GaussianLikelihood(bilby.Likelihood):
def __init__(self, x, y, function, sigma=None):
"""
A general Gaussian likelihood for known or unknown noise - the model
parameters are inferred from the arguments of function
Parameters
----------
x, y: array_like
The data to analyse
function:
The python function to fit to the data. Note, this must take the
dependent variable as its first argument. The other arguments
will require a prior and will be sampled over (unless a fixed
value is given).
sigma: None, float, array_like
If None, the standard deviation of the noise is unknown and will be
estimated (note: this requires a prior to be given for sigma). If
not None, this defined the standard-deviation of the data points.
This can either be a single float, or an array with length equal
to that for `x` and `y`.
"""
self.x = x
self.y = y
self.N = len(x)
self.sigma = sigma
self.function = function
# These lines of code infer the parameters from the provided function
parameters = inspect.getfullargspec(function).args
del parameters[0]
super().__init__(parameters=dict.fromkeys(parameters))
self.parameters = dict.fromkeys(parameters)
self.function_keys = self.parameters.keys()
if self.sigma is None:
self.parameters['sigma'] = None
def log_likelihood(self):
sigma = self.parameters.get('sigma', self.sigma)
model_parameters = {k: self.parameters[k] for k in self.function_keys}
res = self.y - self.function(self.x, **model_parameters)
return -0.5 * (np.sum((res / sigma)**2)
+ self.N*np.log(2*np.pi*sigma**2))
```

We provide this general-purpose class as part of bilby `bilby.core.likelihood.GaussianLikleihood`

An example using this likelihood can be found on this page.

## Common likelihood functions¶

As well as the Gaussian likelihood defined above, bilby provides the following common likelihood functions:

`bilby.core.likelihood.PoissonLikelihood`

`bilby.core.likelihood.StudentTLikelihood`

`bilby.core.likelihood.ExponentialLikelihood`

## Empty likelihood for subclassing¶

We provide an empty parent class which can be subclassed for alternative use
cases `bilby.Likelihood`