Functions
def	loglikelihood.loglikelihood (prob)
	Simply computes the log of the probability. More...

def	loglikelihood.mixedloglikelihood (prob)
	Compute a simulated loglikelihood function. More...

def	loglikelihood.likelihoodregression (meas, model, sigma)
	Computes likelihood function of a regression model. More...

def	loglikelihood.loglikelihoodregression (meas, model, sigma)
	Computes log likelihood function of a regression model. More...

def	weightedloglikelihood.weightedloglikelihood (prob, choice, weight)
	Computes the log likelihood function for the WESML estimator. More...

Detailed Description

Function Documentation

def loglikelihood.likelihoodregression	(	meas,
		model,
		sigma
	)

Computes likelihood function of a regression model.

Parameters

meas	An expression providing the value of the measure for the current observation.
model	An expression providing the output of the model for the current observation.
sigma	An expression (typically, a parameter) providing the standard error $\sigma$ of the error term.

Returns: The likelihood of the regression, assuming a normal distribution, that is
$\frac{1}{\sigma} \phi\left( \frac{y-m}{\sigma} \right)$
where $\phi(\cdot)$ is the pdf of the normal distribution.

Definition at line 50 of file loglikelihood.py.

def loglikelihood.loglikelihood ( prob )

Simply computes the log of the probability.

Parameters

prob	An expression providing the value of the probability.

Definition at line 13 of file loglikelihood.py.

def loglikelihood.loglikelihoodregression	(	meas,
		model,
		sigma
	)

Computes log likelihood function of a regression model.

Parameters

meas	An expression providing the value of the measure for the current observation.
model	An expression providing the output of the model for the current observation.
sigma	An expression (typically, a parameter) providing the standard error $\sigma$ of the error term.

Returns: The likelihood of the regression, assuming a normal distribution, that is
$-\left( \frac{(y-m)^2}{2\sigma^2} \right) - \log(\sigma) - \frac{1}{2}\log(2\pi)$

Definition at line 70 of file loglikelihood.py.

def loglikelihood.mixedloglikelihood ( prob )

Compute a simulated loglikelihood function.

Parameters

prob	An expression providing the value of the probability. Although it is not formally necessary, the expression should contain one or more random variables of a given distribution, and therefore write $P(i\|\xi_1,\ldots,\xi_L)$

Returns: The simulated loglikelihood, given by
$\ln\left(\sum_{r=1}^R P(i|\xi^r_1,\ldots,\xi^r_L) \right)$
where is the number of draws, and $\xi_j^r$ is the rth draw of the random variable $\xi_j$ .

Definition at line 32 of file loglikelihood.py.

def weightedloglikelihood.weightedloglikelihood	(	prob,
		choice,
		weight
	)

Computes the log likelihood function for the WESML estimator.

Parameters

prob	dictionary were the keys are the identifiers of the alternatives in the choice set, and the values are expressions representing the choice probabilities.
choice	expression producing the id of the chosen alternative.
weight	expression producing the id of the chosen alternative.

Definition at line 12 of file weightedloglikelihood.py.