Tutorial: intrinsic distribution

In this tutorial you will learn:

• How to find a intrinsic distribution from data with asymmetric error bars and upper limits

• How to use UltraNest

• How to test whether there are two sub-populations

Lets say we want to find the intrinsic velocity dispersion given some noisy data points.

Our data are velocity measurements of a few globular cluster velocities in a dwarf galaxy.

import numpy as np
# velocity dispersions of dwarf galaxies by van Dokkum et al., Nature, 555, 629 https://arxiv.org/abs/1803.10237v1

values = np.array([15, 4, 2, 11, 1, -2, -1, -14, -39, -3])
values_lo = np.array([7, 16, 6, 3, 6, 5, 10, 6, 11, 13])
values_hi = np.array([7, 15, 8, 3, 6, 6, 10, 7, 14, 14])

n_data = len(values)

Visualise the data

Lets plot the data first to see what is going on:

%matplotlib inline
import matplotlib.pyplot as plt
plt.figure()
xlabel = 'Velocity [km/s]'
plt.xlabel(xlabel)
plt.errorbar(x=values, xerr=[values_lo, values_hi], y=range(n_data),
             marker='o', ls=' ', color='orange')
plt.xlim(-50, 50);

Data properties

This scatter plot shows:

• large, sometimes asymmetric error bars

• intrinsic scatter

Resampling the data

We could also represent each data point by a cloud of samples. Each point represents a possible true solution of that galaxy.

np.random.seed(42)

samples = []

for i in range(n_data):
    # draw normal random points
    u = np.random.normal(size=400)
    v = values[i] + np.where(u < 0, u * values_lo[i], u * values_hi[i])

    samples.append(v)

samples = np.array(samples)

plt.figure()
# for each galaxy, plot alittle cloud with its own colors
plt.violinplot(samples.transpose(), vert=False, showextrema=False)

plt.xlabel(xlabel);

Model

Lets fit a intrinsic, gaussian distribution.

\[y \sim \mathrm{Normal}(\mu, \sigma)\]

The model has two unknown parameters:

• the mean \(\mu\)

• the scatter \(\sigma\)

Lets write down prior ranges for these parameters:

parameters = ['mean', 'scatter']

def prior_transform(cube):
    # the argument, cube, consists of values from 0 to 1
    # we have to convert them to physical scales

    params = cube.copy()
    # let slope go from -3 to +3
    lo = -100
    hi = +100
    params[0] = cube[0] * (hi - lo) + lo
    # let scatter go from 1 to 1000
    lo = np.log10(1)
    hi = np.log10(1000)
    params[1] = 10**(cube[1] * (hi - lo) + lo)
    return params

Define the likelihood, which measures how far the data are from the model predictions. More precisely, how often the parameters would arise under the given parameters.

import scipy.stats

def log_likelihood(params):
    # unpack the current parameters:
    mean, scatter = params

    # compute the probability of each sample
    probs_samples = scipy.stats.norm(mean, scatter).pdf(samples)
    # average over each galaxy, because we assume one of the points is the correct one (logical OR)
    probs_objects = probs_samples.mean(axis=1)
    assert len(probs_objects) == n_data
    # multiply over the galaxies, because we assume our model holds true for all objects (logical AND)
    # for numerical stability, we work in log and avoid zeros
    loglike = np.log(probs_objects + 1e-100).sum()
    return loglike

This likelihood evaluates the model for each object at each sample, and averages the model densities. This corresponds to saying: I don’t know which of these options is correct, but I assume one of them is, so by using a logical OR, I sum the probabilities.

The objects are then combined by multiplying the per-object probabilities. This corresponds to saying: I know the model has to describe all of these objects, so using a logical AND, I multiply the probabilities. Here we convert to log space, and sum. The small number is added to gracefully handle when a object probability is zero (distant means and small sigma).

Solving the problem

import ultranest

sampler = ultranest.ReactiveNestedSampler(parameters, log_likelihood, prior_transform)

Lets first try with relatively poor sampling:

result = sampler.run()

[ultranest] Sampling 400 live points from prior ...
[ultranest] Widening roots to 404 live points (have 400 already) ...
[ultranest] Sampling 4 live points from prior ...

[ultranest] Explored until L=-4e+01
[ultranest] Likelihood function evaluations: 5619
[ultranest]   logZ = -44.5 +- 0.062
[ultranest] Effective samples strategy satisfied (ESS = 1693.4, need >400)
[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.45+-0.07 nat, need <0.50 nat)
[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.06, need <0.5)
[ultranest]   logZ error budget: single: 0.10 bs:0.06 tail:0.01 total:0.06 required:<0.50
[ultranest] done iterating.

from ultranest.plot import cornerplot
cornerplot(result)

# ask for more samples: increasing the required effective sample size
result = sampler.run(min_ess=4000)
cornerplot(result)

[ultranest] Widening roots to 400 live points (have 404 already) ...
[ultranest] Explored until L=-4e+01
[ultranest] Likelihood function evaluations: 5619
[ultranest]   logZ = -44.51 +- 0.06728
[ultranest] Effective samples strategy wants to improve: -49.25..-39.38 (ESS = 1693.4, need >4000)
[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.09 nat, need <0.50 nat)
[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.07, need <0.5)
[ultranest]   logZ error budget: single: 0.10 bs:0.07 tail:0.01 total:0.07 required:<0.50
[ultranest] Widening from 365 to 800 live points before L=-5e+01...
[ultranest] parent value is -inf, so widening roots
[ultranest] Widening roots to 800 live points (have 404 already) ...
[ultranest] Sampling 396 live points from prior ...
[ultranest] Exploring (in particular: L=-inf..-39.38) ...

[ultranest] Explored until L=-4e+01
[ultranest] Likelihood function evaluations: 13116
[ultranest]   logZ = -44.32 +- 0.04247
[ultranest] Effective samples strategy wants to improve: -50.61..-39.38 (ESS = 3325.9, need >4000)
[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.07 nat, need <0.50 nat)
[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.04, need <0.5)
[ultranest]   logZ error budget: single: 0.07 bs:0.04 tail:0.00 total:0.04 required:<0.50
[ultranest] Widening from 704 to 1588 live points before L=-5e+01...
[ultranest] parent value is -inf, so widening roots
[ultranest] Widening roots to 1588 live points (have 800 already) ...
[ultranest] Sampling 788 live points from prior ...
[ultranest] Exploring (in particular: L=-inf..-39.38) ...
[ultranest] Explored until L=-4e+01
[ultranest] Likelihood function evaluations: 25661
[ultranest]   logZ = -44.31 +- 0.03371
[ultranest] Effective samples strategy satisfied (ESS = 6650.2, need >4000)
[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.03 nat, need <0.50 nat)
[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.03, need <0.5)
[ultranest]   logZ error budget: single: 0.05 bs:0.03 tail:0.00 total:0.03 required:<0.50
[ultranest] done iterating.

What is the 3 sigma upper limit on the scatter?

mean_samples, scatter_samples = result['samples'].transpose()

# get 3 sigma quantile
quantile = scipy.stats.norm().cdf(3)

# look at the value:
print('scatter is < %.4f km/s at 3 sigma (%.3f%% quantile)' % (scipy.stats.mstats.mquantiles(scatter_samples, quantile), quantile*100))

scatter is < 30.9846 km/s at 3 sigma (99.865% quantile)

plt.figure()

plt.subplot(2, 1, 1)
plt.errorbar(x=values, xerr=[values_lo, values_hi], y=range(n_data),
             marker='o', ls=' ', color='orange')
plt.xlim(-100, 100)

plt.subplot(2, 1, 2)
plt.xlim(-100, 100)
plt.xlabel(xlabel)

from ultranest.plot import PredictionBand
x = np.linspace(-100, 100, 400)
band = PredictionBand(x)
bandc = PredictionBand(x)

for params in sampler.results['samples'][:40]:
    mean, scatter = params
    band.add(scipy.stats.norm(mean, scatter).pdf(x))

band.shade(color='k', alpha=0.1)
band.line(color='k');

Next

1. Try adjusting the number of live points (min_num_live_points) and effective sample size (min_ess) parameters above to decrease the uncertainties.

2. Try different models – student-t distribution instead of normal, mixture of two gaussians

3. Try varying the prior ranges.

4. Continue with the other tutorials and explore other UltraNest features