Consider a density estimation problem for some *univariate* quantity of
interest, \(\ervy \in \R\). That is, given some contextual information \(\rvx \in \R^{d}\), we wish to find the distribution of \(p(\ervy \vert \rvx)\).

This is a very general setting. For instance, if a component of \(\rvx\) is time \(t\), then we might interpret a sequence of \((\rvx, \ervy)\) values as a time series, and a \(p(\ervy \vert \rvx_{t_{\star}})\) prediction for some future time \(t_{\star}\) as a probabilistic forecast. Alternatively, \(\rvx\) might be the states (and actions) in a reinforcement leaning problem, where \(\ervy\) corresponds to the returns from that state and \(p(\ervy \vert \rvx)\) is the return distribution. Elements of \(\ervx\) might alternatively be latent variables, in which case \(p(\ervy \vert \rvx)\) might be interpreted as the generator / decoder in a latent variable model.

Given the broadness of this problem, it isn’t surprising that a similarly broad
set of solutions has been proposed for estimating this distribution. For the
time series prediction, we might use a Gaussian process, in which case the
predictive distribution \(p(\ervy \vert \rvx_{t_{\star}})\) will be a Gaussian.
In the distributional RL setting, we might use quantile regression
(Dabney *et al.*, 2017), *expectile* regression
(Kostrikov *et al.*, 2021) or a discrete / histogram
distribution (Salimans *et al.*, 2016; Bellemare *et al.*, 2017). For
the generator in a latent variable model, we might use the VAE approach
for parametrising a Gaussian (Kingma *et al.*, 2014).
More generally, a number of *neural* density estimators have been proposed which give
parametric density estimates \(\P_{\vphi}(\ervy \vert \rvx)\), of which the
VAE is an example. Some others include mixture density networks
(Bishop, 1994), energy-based models
(Teh *et al.*, 2003), GANs
(Goodfellow *et al.*, 2014), MADE
(Germain *et al.*, 2015), normalising flows
(Rezende *et al.*, 2015), diffusion models
(Sohl-Dickstein *et al.*, 2015) among numerous others.

These are all suited for different things. Some differentiators to consider
include the estimator’s effectiveness
0: as measured by computational cost and
ability to model the high-dimensional distribution itself
^{0[0]}
in high dimensions,
ability to model distributions of varying ‘complexity’
1: for instance, attributes
such as asymmetry, heavy-tailedness, multi-modality and correlations among
elements
^{1[1]}
, to define an exact density or only statistics of the distribution
and to be able to sample fast or slowly from the learned distribution
2: often
a large contributor to sampling speed is whether it can proceed in parallel or
must be carried out autoregressively
^{2[2]}
.

In this post, I describe an experimental neural density estimation method
for estimating arbitrary
3: that is, with an arbitrary number of modes,
light/heavy tails, asymmetry, etc.
^{3[3]}
univariate distributions
\(\P(\ervy \vert \rvx)\). We begin by reviewing the motivating setting of
quantile regression, before outlining the main idea in Section
2. We then describe some closed form properties of the
distribution—the partition function, likelihood and variance, distribution
and quantile function—and show a simple regression example using this
likelihood. We conclude by discussing a PyTorch implementation.

## Background: Quantile Regression

The proposed method will somewhat resemble quantile
regression (Koenker *et al.*, 1978) and the likelihood can be
understood as a generalisation thereof. Hence, if only to point out this
relationship, we will quickly review QR here. The uninterested reader may
skip to the next section for the main method.

Suppose we have a real-valued random variable \(Y\) with distribution function
\(F_{Y}(y) = \P(Y \le y)\). A *quantile* of \(Y\) is the smallest element in the
support of the distribution for which the distribution function (CDF) is
no less than \(\tau \in (0, 1)\). For instance, common quantiles include the
median, corresponding to \(\tau = 0.5\), the first quartile corresponding to
\(\tau = 0.25\), or the first percentile at \(\tau = 0.01\).

More generally, for a chosen quantile \(\tau \in (0, 1)\), the quantile function \(Q_{Y}(\tau): (0, 1) \to \R\), equivalent to the inverse distribution function, is:

\[Q_{Y}(\tau) = F^{-1}_{Y}(\tau) = \inf \{y\ :\ F_{Y}(y) \ge r\}. \]

Rather than having a single fixed distribution over \(Y\), we might, as in the
setup above, have a vector of covariates, which naturally leads to the
*conditional* quantile function:

\[Q_{Y}(\tau \vert \rvx) = \inf \{y\ :\ \P(Y \le \ervy \mid X = \rvx) \ge \tau\}. \]

**Definition** (Quantile Regression)**.** For some selected quantile \(\tau \in (0, 1)\), a training dataset \(\gD = \{(\rvx_{i}, \ervy_{i})\}^{N}_{i=1}\) and
some model
4: for our purposes, this will almost always be a neural network,
although variants such as Bayesian neural networks and other estimators may be
used.
^{4[4]}
of the conditional quantiles \(Q_{Y}(\tau \vert \rvx) = f_{\vtheta}(\rvx; \tau) + \epsilon\), parametrised by \(\vtheta \in \R^{d}\) and
with corrupting observation noise \(\epsilon\), the task of *quantile
regression* is to estimate the parameters \(\vtheta\) by minimising the expected
*quantile regression loss*, \(\rho_{\tau}(u)\)

\[\begin{align} \rho_{\tau}(u) &= \begin{cases}u(\tau - 1)&\text{if } u < 0 \\ u\tau &\text{otherwise}\end{cases} \\ &= u\big(\tau - \mathbb{I}(u < 0)\big) \label{eq:qr_loss}, \end{align} \]

for \(u = y_{i} - f_{\vtheta}(\rvx_{i})\).

\(\blacksquare\)

In other words, assuming we have i.i.d. data, we wish to find

\[\begin{equation} \label{eq:qr_objective} \vtheta = \argmin_{\hat{\vtheta} \in \Theta} \sum^{N}_{i=1}\rho_{\tau}\big(y_{i} - f_{\hat{\vtheta}}(\rvx_{i}, \tau)\big). \end{equation} \]

### Why does this loss function work?

Due to the connections to the proposed method later, we will dwell on the form of \(\rho_{\tau}\) a little longer.

In supervised learning, we can change the statistic of the distribution \(Y\) that is being predicted by our function approximator \(f_{\vtheta}(\rvx)\), by changing the loss function / the likelihood we maximise. Optimising the usual squared error loss / Gaussian likelihood, \(\argmin_{\hat{\vtheta}\in \Theta}\E_{\gD}\left[\big(y - f(\rvx, \vtheta)\big)^{2}\right]\) leads to an estimator \(f_{\vtheta}\) for the mean. Similarly, when using an absolute deviation loss / Laplace likelihood, \(\argmin_{\hat{\vtheta}\in \Theta}\E_{\gD}\left[\vert y - f(\rvx, \vtheta)\vert\right]\), we get an estimator for the median, or 0.5th quantile.

This median estimator is equivalent
5: up to a scaling constant of \(0.5\)
^{5[5]}
to
Equation \(\ref{eq:qr_objective}\) with \(\rho_{0.5}\). To see this, observe that
an ‘over estimate’, \(y_{i} < f_{\vtheta}(\rvx_{i})\), leading to a negative
residual \(u = y_{i} - f_{\vtheta}(\rvx_{i}) < 0\), will result in the product \(-0.5 u\) in Equation \(\ref{eq:qr_loss}\); hence implementing a (scaled) absolute error.
‘Under estimates’ \(y_{i} > f_{\vtheta}(\rvx_{i})\) leading to positive \(u\) will
simply be scaled by \(0.5\).

By changing \(\tau\), we optimise a skewed absolute deviations loss / asymmetric Laplace likelihood:

Intuitively, the median is the cut point / element that partitions the support into two sets with equal probabilities: it makes sense that positive and negative residuals are weighted equally. When predicting the \(\tau = 0.75\)th percentile however, we want one set to contain \(3/4\) of the probability mass, and the other to contain \(1/4\). Hence, under-estimates (positive residuals) should be three times as costly as over-estimates (negative residual), and the skewed loss with \(\tau = 0.75\) achieves this. The resulting parameters \(\vtheta\) yield a predicted value \(\hat{y} = f_{\vtheta}(\rvx; 0.75)\) which is smaller than samples from \(Y\) one quarter of the time, and larger than \(Y\) samples three quarters of the time.

Finally, for a more thorough verification that this loss works, we can write the expected quantile regression loss, for some estimate of the \(\tau\)th quantile \(\hat{y} = f_{\vtheta}(\rvx; \tau)\), as:

\[\begin{align*} \E[\rho_{\tau}(Y - \hat{y})] &= \int^{\infty}_{-\infty}\rho_{\tau}(y - \hat{y})dF_{Y}(y) \\ &= (\tau - 1) \int_{-\infty}^{\hat{y}} (y - \hat{y}) dF_{Y}(y) + \tau \int_{\hat{y}}^{\infty}(y - \hat{y})dF_{Y}(y). \end{align*} \]

The above is convex, hence we may differentiate it and solve for the value of \(\hat{y}\) at which the loss is minimised. Proceeding using Leibniz’s rule, we get:

\[\begin{align} \frac{d}{d\hat{y}}\E[\rho_{\tau}(Y - \hat{y})] &= (\tau - 1)\left((\hat{y} - \hat{y}) + \int_{-\infty}^{\hat{y}}\frac{\partial}{\partial\hat{y}}(y - \hat{y})d F_{Y}(y)\right) \\ &+ \tau \left(-(\hat{y} - \hat{y}) + \int_{\hat{y}}^{\infty}\frac{\partial}{\partial\hat{y}}(y - \hat{y})d F_{Y}(y)\right) \nonumber \\ &= (1 - \tau)F_{Y}(\hat{y}) - \tau (1 - F_{Y}(\hat{y})) \\ &= F_{Y}(\hat{y}) - \tau \nonumber, \end{align} \]

and so we verify that \(\hat{y} = F^{-1}_{Y}(\tau)\) as required when the
derivative is \(0\).

\(\square\)

### Asymmetric Laplace Likelihood

As mentioned above, we may view the quantile regression loss minimisation as an analogue to maximising an asymmetric Laplace distribution.

The usual Laplace density, parametrised by a location \(\mu\) and scale \(\sigma\) is

\[\begin{equation} \label{eq:laplace_density} f_{\text{L}}(x; \mu, \sigma) = \frac{1}{2\sigma}\exp\left(- \frac{\vert x-\mu \vert}{\sigma}\right). \end{equation} \]

Maximising this likelihood, \(\vtheta = \argmax_{\hat{\vtheta}}\prod^{N}_{i=1}f_{L}(y_{i}; f_{\hat{\vtheta}}(\rvx_{i}, 1))\), where the product comes from the i.i.d. data assumption, and where we have arbitrarily set \(\sigma = 1\), leads to the median estimator \(f_{\vtheta}\).

We may introduce asymmetry into the Laplace distribution with an asymmetry parameter \(\tau \in (0, 1)\) and the following modification to the density (Kozubowski, 1999):

\[\begin{align} f_{\text{ALD}}(x; \mu, \sigma, \tau) &= \frac{\tau(1-\tau)}{\sigma}\exp\left(-\frac{x-\mu}{\sigma}\big(\tau - \mathbb{I}(x - \mu < 0)\big)\right) \\ &\propto \exp \left(-\rho_{\tau}(x - \mu)\right) \label{eq:prop_ald}, \end{align} \]

which we can see is proportional to the negative exponential of the quantile regression loss, \(\rho_{\tau}\).

Values of \(\tau < 0.5\) skew the Laplace distribution to the right
6: that
is, giving it a heavy right tail
^{6[6]}
, while values of \(\tau > 0.5\) skew the
distribution to the left:

### Estimating Multiple Quantiles

Recall that we are interested in characterising the full density of interest, and not merely a few statistics (quantiles). The QR loss above treats \(\tau\) as a fixed hyperparameter of the loss function, thus selecting a single quantile to estimate. However, the full potential of this framework lies not in single-\(\tau\) fits, but simultaneously finding \(Q_{Y}(\tau \vert \rvx)\), \(\forall \tau \in (0, 1)\). Indeed, if one has the distribution function / quantile function, then this does fully describe the distribution.

It is however not trivial to simply specify \(\tau\) as an additional network input, since without adding some additional constraint on the network architecture, there is no guarantee that the network outputs will be monotonic. That is, we would need some mechanism to ensure that \(f_{\vtheta}(\rvx, \tau_{1}) < f_{\vtheta}(\rvx, \tau_{2})\) for all \(\tau_{1} < \tau_{2}\).

To see this, suppose that we naively pick a set of \(M\) different \(\tau\) values of interest, and fit \(M\) separate estimators. This is better than a single \(\tau\)-fit, and still lets us somewhat characterise the distribution at some \(\rvx\). For our dataset \(\gD = \{(\rvx_{i}, \ervy_{i})\}_{i=1}^{N}\) of assumed i.i.d. values, each individual likelihood is:

\[p(\gD \vert \theta, \tau_{m}) = \prod_{i=1}^{N}\frac{\tau(1-\tau_{m})}{\sigma} \exp \left(-\frac{\rho_{\tau_{m}}(y_{i} - f_{\vtheta}(\rvx_{i}))}{\sigma}\right), \]

for \(m \in \{1, \ldots, M\}\).

Training \(M = 20\) such estimators on some synthetic data, for \(\tau\) set to the midpoints of \(21\) linearly spaced points between 0 and 1, and plotting each of the \(f_{\vtheta_{m}}(\cdot, \tau_{m})\) estimators on linearly spaced \(x\) values gives the following:

We can see that the individual model outputs are not monotonic despite the different likelihoods—there is poor sharing of information between the individual estimates. We might try to improve the state of things with a joint prediction of the \(M\) quantiles using a single model \(\rvy = f_{\vtheta}(\rvx, \vtau)\), where element \(\ervy_{i}\) of the output corresponds to \(\ervy_{i} = Q_{Y}(\tau_{i} \vert \rvx)\). The joint likelihood is now

\[\begin{equation} \label{eq:joint_likelihood} p(\gD \vert \vtheta) = \prod_{i=1}^{N} \prod_{j=1}^{M} \frac{\tau_{j}(1-\tau_{j})}{\sigma}\exp \left(-\frac{\rho_{\tau_{j}}\big(y_{i} - f_{\vtheta}(\rvx_{i})\big)}{\sigma}\right). \end{equation} \]

and repeating the above exercise, this results in slightly more coherent
quantile estimates for each test \(\rvx\), however there is still no mechanism to
guarantee that the outputs will be monotonic
7: indeed, they aren’t everywhere
in the plot, with a few lines crossing over each other
^{7[7]}
.

One possible architectural modification to ensure monotonicity is outlined in
*Unconstrained Monotonic Neural Networks*
(Wehenkel *et al.*, 2019), where a positive activation
function is used at the penultimate network layer (such as an ELU activation),
and the output function value is the integral of this result.

However, such methods can be costly and impose undue restrictions on the
function approximator. Further, even if we did solve this problem, we would
still only be characterising a few statistics of the distribution, and not a
closed form density. This necessitates what
Rowland *et al.* (2019) term an *imputation
strategy*, to map the vector of statistics
8: e.g. the vector of \(M\) quantile
estimates
^{8[8]}
to a distribution with those statistics for downstream tasks.

Additionally, the assumption of fixed quantiles might also be needlessly
restrictive. For instance, it might be sensible to vary the \(\tau_{j}\)
locations to place more around regions of interest (for instance around the
mode of the distribution, or along one of the tails) as is proposed in
*implicit quantile networks* (Dabney *et al.*, 2018).

## Piecewise-Linear Log Likelihood Construction

We now come to the main idea, with the following definition of the likelihood.

**Definition** (Piecewise-Linear log likelihood)**.**
Let the distribution over the observed dataset be the following
factorised
9: where we factorise across the \(N\) i.i.d. datapoints, as well as
\(M\) piecewise-linear likelihood components
^{9[9]}
density:

\[\begin{equation} \label{eq:pl3e_likelihood} p(\gD \vert \vtheta) = \prod^{N}_{j=1} \prod^{M}_{i=1} \frac{1}{Z(\vphi^{(j)})}\exp\big(\ell_{\vphi_{i}^{(j)}}(y_{j})\big), \end{equation} \]

where \(N\) are the number of training data points, \(M\) is set to some positive
integer, \(Z(\vphi_{j})\) is a normalising constant
10: written by analogy to the
partition function in an exponential family.
^{10[10]}
, the parameters \(\vphi^{(j)} = \{\alpha_{i}, \beta_{i}, f_{i}\}_{i=1}^{M}\) are the outputs of a neural network
\(\vphi^{(j)} = f_{\vtheta}(\rvx_{j})\) for the \(j\)th data point, and
\(\ell_{\vphi_{i}^{(j)}}\) is
a piecewise-linear function parametrised by the three-element subset of \(\vphi^{(j)}\):

\[\begin{equation} \label{eq:log_likelihood_component} \ell_{\vphi_{i}^{(j)}}(y) \doteq -(y - f_{i})\big(\alpha_{i}\Theta(y - f_{i}) - \beta_{i}\Theta(f_{i} - y)\big), \end{equation} \]

for \(\Theta\) Heaviside step.

\(\blacksquare\)

For clarity, let’s omit the factorisation over the entire dataset, and just consider the form of the log-likelihood for single \((\rvx, y)\) datapoint:

\[\begin{equation} \label{eq:log_likelihood} \log \P(y \vert \rvx) = \sum_{i=1}^{M}\ell_{\vphi_{i}}(y) - \log Z(\vphi),\hspace{2em} \text{for } \vphi = f_{\vtheta}(\rvx). \end{equation} \]

Comparing the negative log likelihood components \(-\ell_{\phi}(y)\) to the quantile regression loss of Equation \(\ref{eq:qr_loss}\), visualised in Section 1.1, we see that our loss function (components) generalise the quantile regression loss in that:

- the left and right gradients needn’t sum to 1
- the discontinuity in the loss needn’t be centred at 0
- we have multiple of them in the loss.

Here are \(M=3\) arbitrary negative log-likelihood / loss components for some randomly selected \(\vphi = \{f_{i}, \alpha_{i}, \beta_{i}\}_{i=1}^M\).

Exponentiating, the individual likelihood components resemble asymmetric ‘Laplace-like’ distributions:

We have written the normalising term in Equation \(\ref{eq:log_likelihood}\) as a log partition function in a nod to the exponential family. Indeed, at first glance it seems like this likelihood should be part of this family which would afford us some convenient properties. However due to the variable \(f_{i}\) terms, we cannot express the term inside the exponent as an inner product between some sufficient statistics and natural parameters. One solution might be to fix the \(f_{i}\) values according to some spacing or schedule, which we might update using some EM procedure. I’ll leave this for future work.

### Normalising the Distribution

To ensure that the above is a valid likelihood, we must compute the normalising term, \(Z(\vphi)\). With some constraints on the values of the parameters \(\valpha\) and \(\vbeta\), as well as an assumption that the \(f_{i}\) are ordered; that is \(f_{i} \le f_{i+1}\), we can compute this normalising function in closed form.

We proceed by integrating the log-density in Equation \(\ref{eq:log_likelihood}\), which we denote \(\gL \doteq \log \P(y \vert \rvx)\). Observe that we have \(M+1\) segments, which we may handle separately. The log likelihood of the \(j\)th segment, where \(y \in (f_{j}, f_{j+1}]\) is found by summing the individual piecewise-linear components between \(f_{j}\) and \(f_{j+1}\):

\[\begin{align} \gL(f_{j} < y \le f_{j+1}) &= -\sum_{i=1}^{j}\alpha_{i}(y - f_{i}) + \sum_{i=j+1}^{M}\beta_{i}(y - f_{i}) - \log Z_{j} \nonumber \\ &= \left(\sum_{i=j+1}^{M}\beta_{i} - \sum_{i=1}^{j}\alpha_{i}\right)y + \left(\sum_{i=1}^{j}\alpha_{i}f_{i} - \sum_{i=j+1}^{M}\beta_{i}f_{i}\right) - \log Z_{j} \nonumber \\ &\doteq a_{j}y + b_{j} - \log Z_{j}, \end{align} \]

where for notational brevity, we have defined the coefficients for the \(j\)th interval (the terms in the parentheses) as \(a_{j}\) and \(b_{j}\).

We must also consider the leftmost segment, where \(y \in (-\infty, f_{1}]\)

\[\begin{align} \gL(-\infty < y \le f_{1}) &= \sum_{i=1}^{M}\beta_{i}(y - f_{i}) - \log Z_{0} \nonumber \\ &= \underbrace{\sum_{i=1}^{M}\beta_{i}y}_{a_{0}} \underbrace{-\sum_{j=1}^{M}\beta_{i}f_{i}}_{b_{0}} - \log Z_{0} \label{eq:leftmost_segment}, \end{align} \]

as well as the rightmost segment for \(y \in (f_{M}, \infty)\)

\[\begin{align} \gL(f_{M} < y < \infty) &= -\left(\sum_{i=1}^{M}\alpha_{i}(y - f_{i})\right) - \log Z_{M} \nonumber \\ &= \underbrace{-\sum_{i=1}^{M}\alpha_{i}y}_{a_{M}} + \underbrace{\sum_{i=1}^{M}\alpha_{i}f_{i}}_{b_{M}} - \log Z_{M} \label{eq:rightmost_segment}, \end{align} \]

where we can see that \(a_{0}\), \(b_{0}\) as well as \(a_{M}\) and \(b_{M}\) are just special cases of the definitions of \(a_{j}\) and \(b_{j}\) given above.

We may now find the normalising term of a distribution parametrised by \(\vphi = \{\vf, \valpha, \vbeta\}\) by summing the integrals of the PDF between each \(f_{j}\), giving special consideration to the integrals at the endpoints from \(-\infty\) to \(f_{1}\), as well as from \(f_{M}\) to \(\infty\).

Beginning with the simpler case where \(f_{j} < y \le f_{j+1}\), we have

\[\begin{align} \int_{f_{j}}^{f_{j+1}}dy\ e^{a_{j}y + b_{j}} &= \left[\frac{1}{a_{j}}e^{a_{j}y + b_{j}}\right]_{f_{j}}^{f_{j+1}} \nonumber \\ &= \frac{1}{a_{j}}e^{b_{j}}\left(e^{a_{j}f_{j+1}} - e^{a_{j}f_{j}}\right). \end{align} \]

### Note

It is possible, although hopefully unlikely, that some \(a_{j} = 0\). For non-probabilistic function approximators \(f_{\vtheta}(\rvx)\), we may resolve to add some small dithering term \(\epsilon \sim \gN(0, 1^{-n})\) for \(n \gg 0\). If we have a probabilistic model, e.g. a BNN, then we can try to re-sample \(\vtheta\) and get a new \(a_{j}\).

For the first edge case where \(y \in (-\infty, f_{1}]\), we have

\[\begin{align} \lim_{\xi \to -\infty} \int_{\xi}^{f_{1}}dy\ e^{a_{0}y + b_{0}} &= \lim_{\xi \to -\infty}\left[\frac{1}{a_{0}}e^{a_{0}y + b_{0}}\right]_{\xi}^{f_{1}} \nonumber \\ &= \lim_{\xi \to -\infty}\frac{1}{a_{0}}e^{b_{0}}\left(e^{a_{0}f_{1}} - e^{a_{0}\xi}\right) \nonumber \\ &= \frac{1}{a_{0}}e^{a_{0}f_{1} + b_{0}} \label{assumption:1} \tag{A1}, \end{align} \]

where the final equality above (\ref{assumption:1}) holds so long as \(a_{0} > 0\). In order to satisfy this condition, recall that in the evaluation of the leftmost segment above (Equation \(\ref{eq:leftmost_segment}\)) we had that \(a_{0} = \sum_{1=1}^{M}\beta_{i}\). In particular, observe that \(\beta_{1}\) only features in the calculations for \(a_{0}\) and \(b_{0}\). Hence, we may treat \(\beta_{1}\) as a ‘free parameter’ which we use to ensure that \(a_{0} > 0\). Writing \(\beta_{1} + \sum_{i=2}^{M}\beta_{i} > 0\), we can therefore set \(\beta_{1} = -\sum_{i=2}^{M}\beta_{i} + \epsilon^{+}\), for some small positive \(\epsilon^{+}\); ensuring numerical stability.

Similarly, for the segment where \(y \in (f_{M}, \infty)\), we have

\[\begin{align} \lim_{\xi \to \infty}\int_{f_{M}}^{\xi}dy\ e^{a_{M}y + b_{M}} &= \lim_{\xi\to\infty}\left[\frac{1}{a_{M}}e^{a_{M}y + b_{M}}\right]_{f_{M}}^{\xi} \nonumber \\ &= \lim_{\xi \to \infty}\frac{1}{a_{M}}e^{b_{M}}\left(e^{a_{M}\xi} - e^{a_{M}f_{M}}\right) \nonumber \\ &= -\frac{1}{a_{M}}e^{a_{M}}f_{M} + b_{M} \label{assumption:2} \tag{A2}, \end{align} \]

with the assumption in (\ref{assumption:2}) now being that \(a_{M} < 0\). As above, from Equation \ref{eq:rightmost_segment} we observe that \(a_{M} = -\sum^{M}_{i=1} \alpha_{i}\), with \(\alpha_{M}\) now being the ‘free parameter’; appearing in the calculations for \(a_{M}\) and \(b_{M}\). Setting \(\alpha_{M} = -\sum_{i=1}^{M-1}\alpha_{i} + \epsilon^{+}\) makes the assumption hold.

We may now explicitly write down the normalising term / partition function \(Z(\vphi)\) as the sum of these integrals:

\[\begin{align} Z(\vphi) &= \int_{-\infty}^{f_{1}}dy\ e^{a_{0}y+b_{0}} + \sum_{j=1}^{M}\int_{f_{j}}^{f_{j+1}}dy\ e^{a_{j}y + b_{j}} + \int_{f_{M}}^{\infty}dy\ e^{a_{M}y + b_{M}} \\ &= \frac{1}{a_{0}}e^{a_{0}f_{1} + b_{0}} + \sum_{j=1}^{M}\frac{1}{a_{j}}e^{b_{j}}\left(e^{a_{j}f_{j+1}} - e^{a_{j}f_{j}}\right) - \frac{1}{a_{M}}e^{a_{M}f_{M} + b_{M}}. \end{align} \]

### Implementation Note

To implement this efficiently, observe that each of the coefficients \(a_{j}\) and \(b_{j}\) are made up of the same underlying sums of terms. Hence a simple dynamic programming algorithm which computes the partial sums of the coefficients takes the time to compute the normalising term from \(O(M^{2})\) for a naive implementation to \(O(M)\).

That said, while this is nice and all, the size of \(M\) is never very large, and on modern computers this optimisation is rather inconsequential, particularly when compared to the cost of running the big neural network \(f_{\vtheta}(\rvx)\) that produces \(\vphi\).

## Moments of the Distribution

One of the benefits of this simple piecewise linear log-likelihood is that the integrals under the resulting distribution are relatively easy to compute. This allows us to find closed form expressions for various moments of the distribution.

For instance, the mean can be found by \(\E_{Y}[y] = \int_{-\infty}^{\infty}y f_{Y}(y) dy\). This integral is relatively long and un-enlightening (I have put it in the appendix). The expression for the mean comes to:

\[\begin{align} \E_{Y}[y] &= \int_{-\infty}^{\infty}y f_{Y}(y)dy \nonumber \\ &= \frac{1}{Z(\vphi)}\Bigg(\frac{e^{a_{0}f_{1} + b_{0}}(a_{0}f_{1} - 1)}{a^{2}_{0}} \nonumber \\ &+\sum_{j=1}^{M-1}\frac{e^{b_{j}}\big(e^{a_{j}f_{j+1}}(a_{j}f_{j+1}-1) - e^{a_{j}f_{j}}(a_{j}f_{j}-1)\big)}{a^{2}_{j}} \nonumber \\ &-\frac{e^{a_{M}f_{M} + b_{M}}(a_{M}f_{M} - 1)}{a^{2}_{M}}\Bigg). \label{eq:mean} \end{align} \]

The variance or second moment of the distribution, found through a similarly long integral to the above which is also in the appendix, comes to

\[\begin{align} \Var(Y) &= \int_{-\infty}^{\infty}y^{2}f_{Y}(y)dy - \E_{Y}[y]^{2}\nonumber \\ &= \frac{1}{Z(\vphi)}\Bigg(\frac{e^{a_{0}f_{1} + b_{0}}\big(a_{0}^{2}f_{1}^{2} - 2a_{0}f_{1} + 2\big)}{a_{0}^{3}} \nonumber \\ &+ \sum_{j=1}^{M-1} \frac{e^{b_{j}}\big(e^{a_{j}f_{j+1}}(a_{j}^{2}f_{j+1}^{2} - 2a_{j}f_{j+1} + 2) - e^{a_{j}f_{j}}(a^{2}_{j}f^{2}_{j} - 2a_{j}f_{j} + 2)\big)}{a^{3}_{j}} \nonumber \\ &-\frac{e^{a_{M}f_{M}+b_{M}}\big(a^{2}_{M}f^{2}_{M} - 2a_{M}f_{M} + 2\big)}{a^{3}_{M}}\Bigg). \end{align} \]

## Regression Example

We can use this likelihood to do regression. For example, consider the
following data from Fanaee-T *et al.* (2013) which shows the
number of daily rentals for a bike sharing scheme.

We can observe a number of fluctuations due to seasonality, along with a broader upward trend, with differences in variance throughout. While the distribution appears to be mostly unimodal, it provides a reasonably interesting application.

Fitting a small neural network using maximum likelihood results in the following:

In the above figure, we compute the distribution for \(100\) equally spaced dates along the x axis. We plot the mean of these distributions, along with the 0.05th, 0.15th, 0.25th and 0.35th quantiles in the shaded regions to characterise the shape of the distribution at each point. The equations for the mean and quantiles (inverse CDF) are derived below.

Notably, the learned distributions do a good job of placing probability mass away from the mean to account for the probability of outlier rental counts; for instance on sunny days or when there’s an event in town. It should not be surprising that the distributions show heavy tails, due to the likelihood’s similarity to the Laplace distribution.

## Inverse Transform Sampling

We can use the inverse of the distribution function (i.e. the quantile
function) to map samples drawn from the unit interval \(U[0, 1]\) onto the
support of the distribution. This method of drawing samples from a distribution
is known as *inverse transform sampling*.

To do inverse transform sampling with our proposed density, we must first find an expression for the distribution function \(F_{Y}(y)\), which we can then invert to find the quantile function \(F^{-1}_{Y}(\tau) = Q_{Y}(\tau)\).

Recall that a distribution function is found by integrating the density from \(-\infty\) to \(y\):

\[F_{Y}(y) = \int_{-\infty}^{y}dt\ f_{Y}(t). \]

Since we have already evaluated this integral when computing the expression for the normalising constant, \(Z(\vphi)\), we may re-use the integrals under the segments \((f_{j}, f_{j+1})\) where \(f_{j+1} < y\). We must then add the integral for the last (partial) segment—noting that this may in fact be the first segment if \(y < f_{1}\).

More formally, let the integer \(K\) for some evaluation point \(y\) be the index of the segment containing \(y\):

\[K \doteq \max \left( \{0\} \cup \{\hat{K} \in \{1, \ldots, M\}\ :\ f_{\hat{K}} \le k\}\right). \]

Now,

\[\begin{align} F_{Y}(y) &= \int_{-\infty}^{y}dt\ f_{Y}(t)\nonumber \\ &= \frac{1}{Z(\vphi)} \begin{cases} \int_{-\infty}^{y}dt\ e^{a_{0}t + b_{0}} &\text{if } y < f_{1} \\ \int_{-\infty}^{f_{1}}dt\ e^{a_{0}t + b_{0}} + \sum_{j=1}^{K-1}\int_{f_{j}}^{f_{j+1}}dt\ e^{a_{j}t + b_{j}} + \int_{f_{K}}^{y}dt\ e^{a_{K}t + b_{K}}& \text{otherwise} \end{cases} \nonumber \\ &= \frac{1}{Z(\vphi)} \begin{cases} \frac{1}{a_{0}}e^{a_{0}y + b_{0}} &\text{if } y < f_{1} \\ \frac{1}{a_{0}}e^{a_{0}f_{1} + b_{0}} + \sum_{j=1}^{K-1}\frac{1}{a_{j}}e^{b_{j}}\big(e^{a_{j}f_{j+1}} - e^{a_{j}f_{j}}\big) + \frac{1}{a_{K}}e^{b_{K}}\big(e^{a_{K}y} - e^{a_{K}f_{K}}\big) &\text{otherwise} \end{cases} \nonumber \\ &= \frac{1}{Z(\vphi)}\Bigg(A + \sum_{j=1}^{K-1}\frac{1}{a_{j}}e^{b_{j}}\big(e^{a_{j}f_{j+1}} - e^{a_{j}f_{j}}\big) + \frac{1}{a_{K}}e^{b_{K}}\big(e^{a_{K}y} - e^{a_{K}f_{K + \mathbb{I}[K=0]}}\big)\Bigg) \label{eq:CDF} \end{align} \]

where we have defined \(A \doteq \frac{1}{a_{0}}e^{a_{0}f_{1} + b_{0}}\) for notational brevity. The terms \(a_{j}\) and \(b_{j}\) are defined for \(j \in [0, M]\) as they were in the previous section which, for completeness, was

\[\begin{align*} a_{j} &= \sum_{i=j+1}^{M}\beta_{i} - \sum_{i=1}^{j}\alpha_{i} \\ b_{j} &= \sum_{i=1}^{j}\alpha_{i}f_{i} - \sum_{i = j + 1}^{M}\beta_{i}f_{i}. \end{align*} \]

Inverting the CDF in Equation \ref{eq:CDF} gives us an analytical expression for the quantile function. We will define a new limit for the summand (previously \(K\)) for some input point \(\tau \in [0, 1]\) as:

\[L \doteq \max \left(\{0\} \cup \{\hat{L} \in \{1, \ldots, M\}\ :\ F_{Y}(f_{\hat{L}}) \le \tau \}\right). \]

Now inverting the CDF gives us the quantile function:

\[\begin{equation} \label{eq:ICDF} F^{-1}_{Y}(\tau) = \frac{\log\left(\left(\tau Z(\vphi) - A - \sum_{j=1}^{L-1}\frac{1}{a_{j}}e^{b_{j}}\big(e^{a_{j}f_{j+1}} - e^{a_{j}f_{j}}\big)\right)a_{L}e^{b_{L}} + e^{a_{L}f_{L + \mathbb{I}[L=0]}}\right)}{a_{L}}. \end{equation} \]

As usual, the normal constraints pertaining to invertability persist: if the
distribution function \(F_{Y}\) is not monotonically increasing at some
point
11: i.e. the distribution has density \(0\) at some point
^{11[11]}
then it is not
invertible. We may also get a zero-valued coefficient \(a_{j}\) for \(j \le L\) if
the number of components \(M\) is large compared to the complexity of the
distribution being modelled. As before, these are relatively uncommon and we
can handle these at runtime by adding some small perturbation \(\epsilon \sim \gN(0, 1^{-n})\), \(n \gg 0\).

With this closed form expression for the quantile function in hand, we can proceed to sample values from the distribution using inverse transform sampling. The figure below shows a fitted density on multi-modal test data:

Here is the corresponding density function:

Drawing samples \(\vtau \sim U[0, 1]\) and mapping these through the quantile function in Equation \ref{eq:ICDF} allows us to sample from the distribution:

Finally, for some applications it may be convenient to find the mode(s) of the distribution. With the PL3E distribution, these will always appear at one of the \(f_{j}\) locations. In particular, we may identify a mode if the density at the neighbouring points \(f_{j-1}\) and \(f_{j+1}\) are smaller than \(f_{j}\). In other words, \(f_{j}\) can be identified as a mode if

\[\begin{align*} f_{Y}(f_{j}) &> f_{Y}(f_{j-1}) \\ a_{j}f_{j} + b_{j} &> a_{j-1}f_{j-1} + b_{j-1} \end{align*} \]

and

\[\begin{align*} f_{Y}(f_{j}) &> f_{Y}(f_{j+1}) \\ a_{j}f_{j} + b_{j} &> a_{j+1}f_{j+1} + b_{j+1}, \end{align*} \]

where we arrive at the above by taking the logarithm (a monotonic function). Note that for the endpoints \(f_{1}\) and \(f_{M}\), we only need the first and second of these comparisons to hold, respectively.

The figure below shows the location of the modes on a density fitted to synthetic data.

## Appendix

### Mean Calculation

Here we evaluate the integral \(\E_{Y}[y] = \int_{-\infty}^{\infty}y f_{Y}(y)\ dy\), where the density function is of the form

\[f_{Y}(y) = e^{a_{j}y + b_{j}}, \]

with \(j \in \{0, \ldots, M\}\) such that \(f_{j} < y \le f_{j+1}\).

We begin by evaluating the indefinite integral \(\int x e^{ax + b}\ dx\). Letting \(u = ax + b\), we have

\[\begin{align*} \int x e^{ax + b}dx &= \frac{1}{a}\int \frac{u - b}{a}e^{u}du \\ &= \frac{1}{a^{2}}\int e^{u}(u-b)du \\ &= \frac{1}{a^{2}}\int e^{u}u - be^{u}du \\ &= \frac{1}{a^{2}}\int e^{u}u\ du - \frac{b}{a^{2}}\int e^{u}du. \end{align*} \]

We can integrate the first term by parts, setting \(f = u\) and \(dg = e^{u}\). Now,

\[\begin{align*} \frac{1}{a^{2}} \int e^{u}u\ du - \frac{b}{a^{2}} \int e^{u}du &= \frac{1}{a^{2}}\left(e^{u}u - \int e^{u}\ du\right) - \frac{b}{a^{2}}\int e^{u} du \\ &= \frac{e^{u}u}{a^{2}} + \left(-\frac{b}{a^{2}} - \frac{1}{a^{2}}\right)\int e^{u}\ du \\ &= \frac{e^{u}u}{a^{2}} - \frac{(b+1)e^{u}}{a^{2}} + c \\ &= \frac{e^{ax + b}(ax + b)}{a^{2}} - \frac{(b+1)e^{ax + b}}{a^{2}} + c \\ &= \frac{e^{ax + b}(ax - 1)}{a^{2}} + c. \end{align*} \]

Having found this indefinite integral for the expectation of individual segments of the distribution, we can find the integral of each segment from \(-\infty\) to \(\infty\). As previously, we consider three cases: for the first when \(-\infty < y < f_{1}\) we have

\[\begin{align*} \lim_{\xi \to -\infty}\int_{\xi}^{f_{1}}y f_{Y}(y)\ dy &= \lim_{\xi \to -\infty}\int_{\xi}^{f_{1}} y e^{a_{0}y + b_{0}}dy \\ &= \lim_{\xi \to -\infty}\left[\frac{e^{a_{0}y + b_{0}}(a_{0}y - 1)}{a^{2}_{0}}\right]_{\xi}^{f_{1}} \\ &= \lim_{\xi \to -\infty} \frac{e^{a_{0}f_{1} + b_{0}}(a_{0}f_{1} - 1)}{a^{2}_{0}} - \frac{e^{a_{0}\xi + b_{0}}(a_{0}\xi - 1)}{a^{2}_{0}} \\ &= \frac{e^{a_{0}f_{1} + b_{0}}(a_{0}f_{1} - 1)}{a^{2}_{0}} - \lim_{\xi \to -\infty}\left(\frac{e^{a_{0}\xi + b_{0}}a_{0}\xi}{a^{2}_{0}} - \frac{e^{a_{0}\xi + b_{0}}}{a^{2}_{0}}\right) \\ &= \frac{e^{a_{0}f_{1} + b_{0}}(a_{0}f_{1} - 1)}{a^{2}_{0}} - \lim_{\xi \to -\infty}\frac{\xi}{e^{-(a_{0}\xi + b_{0})}a_{0}}, \end{align*} \]

where the final step above follows from the fact that \(a_{0} > 0\). Now, by L’Hôpital’s rule,

\[\lim_{\xi \to -\infty} \frac{\frac{d}{d\xi}\xi}{\frac{d}{d\xi}e^{-(a_{0}\xi + b_{0})}a_{0}} = \lim_{\ell \to -\infty} \frac{1}{-a_{0}^{2}e^{-a_{0}\xi} - b_{0}} = 0, \]

since, once again, \(a_{0} > 0\). Hence, for the first integral, we get

\[\begin{equation} \label{eq:first_mean_integral} \lim_{\xi \to -\infty}\int_{\xi}^{f_{1}}y f_{Y}(y) dy = \frac{e^{a_{0}f_{1} + b_{0}}(a_{0}f_{1} - 1)}{a_{0}^{2}}. \end{equation} \]

For the second case where \(f_{j} \le y < f_{j+1}\) for some \(j \in \{1, \ldots, M\}\), we have

\[\begin{align*} \int_{f_{j}}^{f_{j+1}}y f_{Y}(y)dy &= \left[\frac{e^{a_{j}y + b_{j}}(a_{j}y - 1)}{a^{2}_{j}}\right]^{f_{j+1}}_{f_{j}} \\ &= \frac{e^{a_{j}f_{j+1} + b_{j}}(a_{j}f_{j+1} - 1)}{a^{2}_{j}} - \frac{e^{a_{j}f_{j} + b_{j}}(a_{j}f_{j} - 1)}{a_{j}^{2}} \\ &= \frac{e^{b_{j}}\big(e^{a_{j}f_{j+1}}(a_{j}f_{j+1} - 1) - e^{a_{j}f_{j}}(a_{j}f_{j} - 1)\big)}{a^{2}_{j}}. \end{align*} \]

Finally, for the last case where \(f_{M} \le y < \infty\), we have

\[\begin{align*} \lim_{\xi \to \infty}\int_{f_{M}}^{\xi}y f_{Y}(y)\ dy &= \lim_{\xi \to \infty}\left[\frac{e^{a_{M}y + b_{M}}(a_{M}y - 1)}{a^{2}_{M}}\right]^{\xi}_{f_{M}} \\ &= \lim_{\xi \to \infty} \frac{e^{a_{M}\xi + b_{M}}(a_{M}\xi - 1)}{a^{2}_{M}} - \frac{e^{a_{M}f_{M} + b_{M}}(a_{M}f_{M} - 1)}{a^{2}_{M}} \\ &= \lim_{\xi \to \infty} \frac{e^{a_{M}\xi + b_{M}}a_{M}\xi}{a^{2}_{M}} - \frac{e^{a_{M}\xi + b_{M}}}{a^{2}_{M}} - \frac{e^{a_{M}f_{M} + b_{M}}(a_{M}f_{M} - 1)}{a^{2}_{M}} \\ &= \lim_{\xi \to \infty} \frac{e^{a_{M}\xi + b_{M}}a_{M}\xi}{a^{2}_{M}} - \frac{e^{a_{M}f_{M} + b_{M}}(a_{M}f_{M} - 1)}{a^{2}_{M}} \\ &= -\frac{e^{a_{M}f_{M} + b_{M}}(a_{M}f_{M} - 1)}{a^{2}_{M}}. \end{align*} \]

where the penultimate line follows from the fact that \(a_{M} < 0\), and the last is arrived at by L’Hôpital’s rule, as above.

Multiplying by the reciprocal of the normalising constant, we obtain the expression for the expected value:

\[\begin{align*} \E_{Y}[y] &= \frac{1}{Z(\vphi)}\Bigg(\frac{e^{a_{0}f_{1} + b_{0}}(a_{0}f_{1} - 1)}{a^{2}_{0}} \\ &+\sum_{j=1}^{M-1}\frac{e^{b_{j}}\big(e^{a_{j}f_{j+1}}(a_{j}f_{j+1}-1) - e^{a_{j}f_{j}}(a_{j}f_{j}-1)\big)}{a^{2}_{j}} \\ &-\frac{e^{a_{M}f_{M} + b_{M}}(a_{M}f_{M} - 1)}{a^{2}_{M}}\Bigg). \end{align*} \]

\(\square\)