aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
data = tf.constant([10., 5.], dtype=tf.float32)
rv_poisson = tfd.Poisson(rate=tf.reduce_mean(data))## instead of -->>rv_poisson = tfd.Poisson(rate=1./tf.reduce_mean(data)) in Poisson rate is mean and in Exponential it should be 1/mean
poisson = rv_poisson.sample()
Build graph
data = tf.constant([10., 5.], dtype=tf.float32) rv_poisson = tfd.Poisson(rate=tf.reduce_mean(data))## instead of -->>rv_poisson = tfd.Poisson(rate=1./tf.reduce_mean(data)) in Poisson rate is mean and in Exponential it should be 1/mean poisson = rv_poisson.sample()