Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

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chapter 3,the cluster prediction’s issue.

1

the print “Probability of belongs to cluster 1:” should be “Probability of belongs to cluster 0:” ?? or i miss the meanings?

uammy

Allows a user to successfully run the top setup cell of Ch1_Introduction_TFP. TensorFlow version is pinned to 1.13.1, the version running currently in Colab.

austinbrian

I am using google colab. How to you activate GPU usage for MCMC sampling? I am finding it very slow. And as building graphs is supposed to be faster than...

bluesky314

Logistic Example

3

In ch2 Logistic example, when we assume our data follows logistic, why then use Bernoulli after that? In previous examples we only had an assumption of the data distribution and...

bluesky314

A/B Testing Joint Explanation

1

The double_joint_log_prob in ch2 A/B testing is not explained at all. Why do we return ( rv_prob_A.log_prob(prob_A) + rv_prob_B.log_prob(prob_B) + tf.reduce_sum(rv_obs_A.log_prob(observations_A)) + tf.reduce_sum(rv_obs_B.log_prob(observations_B)) ) would'nt this corrospond to finding the...

bluesky314

Chapter 1 | sampling from the chain | TFP version

10

Hi there, on my system with tf-gpu this code makes trouble: ` [ lambda_1_samples, lambda_2_samples, posterior_tau, ], kernel_results = tfp.mcmc.sample_chain( num_results=1000, num_burnin_steps=100, current_state=initial_chain_state, kernel=tfp.mcmc.TransformedTransitionKernel( inner_kernel=tfp.mcmc.HamiltonianMonteCarlo( target_log_prob_fn=unnormalized_log_posterior, num_leapfrog_steps=2, step_size=step_size, step_size_update_fn=tfp.mcmc.make_simple_step_size_update_policy(), state_gradients_are_stopped=True),...

Pindar777

Chapter 3 | use of tfd.Independend | TFP version

1

Hi @matthew-mcateer, could you please elaborate on the indtroduction of these lines in Chapter 3? `sds = tfd.Independent(tfd.Uniform(low=[0., 0.], high=[100., 100.]), reinterpreted_batch_ndims=1, name='sds')` At the moment the texts says >...

Pindar777

Chapter 2 TFP. make_simple_step_size_update_policy requires positional argument (doesn't run on colab)

6

Running from colab. The TFP make_simple_step_size_update_policy requires a positional argument. Providing a value for the parameter "num_adaptation_steps:, produces different errors. Here is the original error message. WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from...

Liviodl

Chapter 1 | Mandatory coin-flip mislabeled prior | TFP version

1

Hi there, The first line of the code for the Mandatory coin-flip example creates a random variable as follows: ``rv_coin_flip_prior = tfp.distributions.Bernoulli(probs=0.5, dtype=tf.int32)`` I think the variable name implies that...

arastuie

p1 = tfd.Uniform(name='p', low=0., high=1.).sample() p2 = 1 - p1 p = tf.stack([p1, p2]) assignment = tfd.Categorical(name="assignment", probs=p) assignment_ = evaluate(assignment.sample(sample_shape=data_.shape[0], seed=42))[:10] print("prior assignment, with p = %.2f:" % evaluate(p1))...

leidawt