glm_families: GLM families

In tfprobability: Interface to 'TensorFlow Probability'

GLM families

Description

A list of models that can be used as the model argument in glm_fit():

Details

• Bernoulli: Bernoulli(probs=mean) where mean = sigmoid(matmul(X, weights))

• BernoulliNormalCDF: Bernoulli(probs=mean) where mean = Normal(0, 1).cdf(matmul(X, weights))

• GammaExp: Gamma(concentration=1, rate=1 / mean) where mean = exp(matmul(X, weights))

• GammaSoftplus: Gamma(concentration=1, rate=1 / mean) where mean = softplus(matmul(X, weights))

• LogNormal: LogNormal(loc=log(mean) - log(2) / 2, scale=sqrt(log(2))) where mean = exp(matmul(X, weights)).

• LogNormalSoftplus: LogNormal(loc=log(mean) - log(2) / 2, scale=sqrt(log(2))) where mean = softplus(matmul(X, weights))

• Normal: Normal(loc=mean, scale=1) where mean = matmul(X, weights).

• NormalReciprocal: Normal(loc=mean, scale=1) where mean = 1 / matmul(X, weights)

• Poisson: Poisson(rate=mean) where mean = exp(matmul(X, weights)).

• PoissonSoftplus: Poisson(rate=mean) where mean = softplus(matmul(X, weights)).

Value

list of models that can be used as the model argument in glm_fit()

See Also

Other glm_fit: glm_fit.tensorflow.tensor(), glm_fit_one_step.tensorflow.tensor()

tfprobability documentation built on Sept. 1, 2022, 5:07 p.m.