I am trying to implement Bayesian Logistic Regression in greta, but I’m stuck at defining the likelihood. Here is the code so far,

```
library(greta)
# data
summary(iris)
x = within(iris, rm(Species))
y = iris$Species
# prior
w_mean = t(integer(5))
w_sigma = diag(5)
prior = multivariate_normal(w_mean, w_sigma)
# likelihood
distribution(y) = # how to define logistic regression likelihood
# model
m = model(prior)
# sampling
draws <- mcmc(m, n_samples = 1000)
```

Because iris has 4 predictors, I have taken prior as multivariate normal distribution with 5 means(one extra for bias) and covariance as 5x5 identity matrix.

Now I need to define likelihood, which is given as mentioned here. I am confused as to how I would implement this.