# likelihoodHawkes: Compute the likelihood function of a hawkes process In hawkes: Hawkes process simulation and calibration toolkit

## Description

Compute the likelihood function of a hawkes process for the given parameter and given the jump times vector (or list of vectors in the multivariate case), and until a time horizon.

## Usage

 `1` ```likelihoodHawkes(lambda0, alpha, beta, history) ```

## Arguments

 `lambda0` Vector of initial intensity, a scalar in the monovariate case. `alpha` Matrix of excitation, a scalar in the monovariate case. Excitation values are all positive. `beta` Vector of betas, a scalar in the monovariate case. `history` Jump times vector (or list of vectors in the multivariate case).

## Value

Returns the opposite of the likelihood.

## References

Y. Ogata. (1981) On Lewis simulation method for point processes. IEEE Transactions on Information Theory, 31

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```#One dimensional Hawkes process lambda0<-0.2 alpha<-0.5 beta<-0.7 history<-simulateHawkes(lambda0,alpha,beta,3600) l<-likelihoodHawkes(lambda0,alpha,beta,history[[1]]) #Multivariate Hawkes process lambda0<-c(0.2,0.2) alpha<-matrix(c(0.5,0,0,0.5),byrow=TRUE,nrow=2) beta<-c(0.7,0.7) history<-simulateHawkes(lambda0,alpha,beta,3600) l<-likelihoodHawkes(lambda0,alpha,beta,history) ```

### Example output

```
```

hawkes documentation built on May 2, 2019, 6:39 a.m.