v0.8

v0.8

Non-visible backend changes

• Distributions are now implemented as classes (rather than lists).

Bayesian library updates

• [Breaking change] bayes.update now updates normal distributions from the distribution rather than from samples.
• [Breaking change] bayes.update no longer takes a type parameter but can now infer the type from the passed distribution.
• [Breaking change] Corrected a bug in how bayes.update implemented evidence_weight when updating normal distributions.

v0.7

v0.7

Bugfixes

• Fixes an issue with sampling from the bernoulli distribution.
• Fixes a bug with the implementation of lclip and rclip.

New distributions

• Adds discrete to calculate a discrete distribution. Example: discrete({'A': 0.3, 'B': 0.3, 'C': 0.4}) will return A 30% of the time, B 30% of the time, and C 40% of the time.
• Adds poisson(lam) to calculate a poisson distribution.
• Adds gamma(size, scale) to calculate a gamma distribution.

Bayesian library updates

• Adds bayes.bayesnet to do bayesian inferece (see README).
• bayes.update now can take an evidence_weight parameter. Typically this would be equal to the number of samples.
• [Breaking change] bayes.bayes has been renamed bayes.simple_bayes.

Other

• [Breaking change] credibility, which defines the size of the interval (e.g., credibility=0.8 for an 80% CI), is now a property of the distribution rather than the sampler. That is, you should now call sample(norm(1, 3, credibility=0.8)) whereas previously it was sample(norm(1, 3), credibility=0.8). This will allow mixing of distributions with different credible ranges.
• [Breaking change] Numbers have been changed from functions to global variables. Use thousand or K instead of thousand() (old/deprecated).
• sample now has a nice progress reporter if verbose=True.
• The exponential distribution now implements lclip and rclip.
• The mixture distribution can infer equal weights if no weights are given.
• The mixture distribution can infer the last weight if the last weight is not given.
• geomean and geomean_odds can infer the last weight if the last weight is not given.
• You can use flip_coin and roll_die(sides) to flip a coin or roll a die.
• event_happens and event are aliases for event_occurs.
• get_percentiles will now cast output to int if digits=0.
• get_log_percentiles now has a default value for percentiles.
• You can now set the seed for the RNG using sq.set_seed.

Non-visible backend changes

• Now has tests via pytest.
• The random numbers now come from a numpy generator as opposed to the previous deprecated np.random methods.
• The sample module (containing the sample function) has been renamed samplers.

v0.6

v0.6

New distributions

• Add triangular(left, mode, right) to calculate a triangular distribution.
• Add binomial(n, p) to calculate a binomial distribution.
• Add beta(a, b) to calculate a beta distribution.
• Add bernoulli(p) to calculate a bernoulli distribution.
• Add exponential(scale) to calculate an exponential distribution.

New Bayesian library

• Add bayes.update to get a posterior distribution from a prior distribution and an evidence distribution.
• Add bayes.average to average distributions (via a mixture).

New utility functions

• Add laplace to calculate Laplace's Law of Succession. If s and n are passed, it will calculate (s+1)/(n+2). If s, time_passed, and time_remaining are passed, it will use the time invariant version. Use time_fixed=True for fixed time periods and time_fixed=False (default) otherwise.
• Add geomean to calculate the geometric mean.
• Add p_to_odds to convert probability to odds. Also odds_to_p to convert odds to probability.
• Add geomean_odds to calculate the geometric mean of odds, converted to and from probabilities.

Other

• If a distribution is defined with sd but not mean, mean will be inferred to be 0.
• sample can now take lclip and rclip directly, in addition to defining lclip and rclip on the distribution itself. If both are defined, the most restrictive of the two bounds will be used.

v0.5

v0.5

• Fix critical bug to tdist and log_tdist introduced in v0.3.

v0.4

v0.4

• Fix critical bug introduced in v0.3.

v0.3

v0.3

• Be able to define distributions using mean and sd instead of defining the interval.

v0.2

v0.2

• Change distributed_log to mixture (to follow Squiggle) and allow it to implement any sub-distribution.
• Changed library to single import.
• Remove weighted_log as a distribution.

v0.1

v0.1

• Initial library