---
title: "Likelihood calculation"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Likelihood calculation}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  markdown: 
    wrap: 72
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 7,
  fig.align = "center"
)
```

```{r setup}
library(mixvlmc)
library(geodist) ## used in the earth quake example
library(ggplot2) ## ditto
```

Most of the theoretical papers on Variable Length Markov Chains (VLMC,
see `vignette("variable-length-markov-chains")`) focus on time series
indexed by $\mathbb{Z}$ which simplifies the analysis. In practice time
series are obviously finite which introduces some difficulties
associated to the initial observations. In particular, the definition of
a likelihood function is not entirely obvious.

This vignette discusses the case of VLMC, but the discussion applies to 
VLMC with covariates with minimal adaptation. 

## Executive summary
For model selection, we recommend to use a likelihood function that disregards
the first few observations for which a (CO)VLMC cannot provide a context. This is
based on the *truncated* likelihood function. For all practical uses such as
prediction and sampling, we recommend to use a notion of *extended* contexts. 

## Likelihood functions for Markov chains

Let us consider a doubly infinite time series $X_{i\in\mathbb{Z}}$
generated by some model $\mathcal{M}$. The likelihood function
associated to a finite observation of the time series,
$(x_i)_{1\leq i\leq n}$, is
$\mathbb{P}_{\mathcal{M}}(X_1=x_1,\ldots,X_n=x_n)$.

If $\mathcal{M}$ is a Markov chain of order $d$, then we have $$
\mathbb{P}_{\mathcal{M}}(X_{d+1}=x_{d+1},\ldots,X_n=x_n)=\prod_{i=d+1}^n\mathbb{P}_{\mathcal{M}}(X_i=x_i|X_{i-1}=x_{i-1},\ldots,X_{i-d}=x_{i-d}).
$$ Thus if we know only $x_{1\leq i\leq n}$ we can compute only the
likelihood of $(x_i)_{d+1\leq i\leq n}$.

In practice, comparing models with different orders should be done only
using the likelihood functions based on the same subset of the observed
time series, i.e. using the highest order. As pointed out in several
papers this has no impact on asymptotic results (see e.g. [Garivier, A.
(2006), Consistency of the unlimited BIC context tree estimator. IEEE
Transactions on Information Theory, 52 (10)
4630--4635](https://dx.doi.org/10.1109/TIT.2006.881742)).

## Likelihood functions for VLMC

### Truncated solution

The simplest way to define a likelihood function for a single VLMC is to
consider it as a Markov chain of the order given by the length of its
longest context (i.e. of the order of the VLMC).

### Specific contexts

Another approach considers the fact that the past of
$(x_i)_{1\leq i\leq n}$ is unknown and replace it by a collection of
specific contexts that summarize this unknown past. This is used in
Garivier's paper cited above. By definition, each observation that does
not have a actual context in the VLMC appears only once and thus is
perfectly predicted by the empirical distribution associated to it. It
has therefore a likelihood of 1. In practice, this amounts to
identifying $\mathbb{P}_{\mathcal{M}}(X_1=x_1,\ldots,X_n=x_n)$ to
$\mathbb{P}_{\mathcal{M}}(X_{d+1}=x_{d+1},\ldots,X_n=x_n)$.

In terms of parameters for AIC/BIC calculation, this corresponds to
adding a specific parameter for each of the $d$ initial values, where
$d$ is the order of the VLMC. Notice that this departs from Garivier's approach
cited above (in this paper, each initial value is associated to a full context
and thus to $|S|-1$ parameters if $S$ is the state space).

### Extended contexts

Another approach considers extended/approximate contexts for the $d$
initial values. Indeed each observation $x_i$ for $1\leq i\leq d$ can be
considered has having its context partially determined by
$(x_1,\ldots, x_{i-1})$, in particular by the empty context for $i=1$. Let us
consider for instance $x_1$ and the empty context (the root of the
context tree). If $d\geq 1$, we cannot determine the context of $x_1$
without values of $(x_{-d+1},\ldots, x_0)$ even if the empty context is
a valid one. However, we can assign this empty context to $x_1$ because
of the lack of information. More generally, we can traverse the context
tree using as many past values as available and stop in the
corresponding node which is then interpreted as a context using the
frequencies collected during the construction of the VLMC.

In terms of parameters, this adds to the VLMC an additional extended
context for each node of the context tree which is not a context. For instance, 
in a binary state space $S=\{0, 1\}$, one may consider a Markov Chain of order 1. 
In this case, we have two contexts $0$ and $1$, and the root node of the context
tree is not a proper (empty) context. To compute the likelihood of the first
observation, we need therefore a new extended context, the empty one. 

### Complete example

Let us revisit the California earth quakes example proposed in
`vignette("variable-length-markov-chains")`. The model is obtained as
follows (see the vignette for details):

```{r}
California_centre <- data.frame(longitude = -119.449444, latitude = 37.166111)
distances <- geodist(globalearthquake[, c("longitude", "latitude")],
  California_centre,
  measure = "geodesic"
)
California_earth_quakes <- globalearthquake[distances < 2e6, ] ## distances are in meters
California_weeks <- rep(0, max(globalearthquake$nbweeks))
California_weeks[California_earth_quakes$nbweeks] <- 1
California_weeks_earth_quakes_model <- tune_vlmc(California_weeks,
  initial = "truncated",
  save = "all"
)
model <- as_vlmc(California_weeks_earth_quakes_model)
draw(model, prob = FALSE)
```

The optimal model according to the BIC has an order of `r depth(model)`
and thus the simple truncated log likelihood is obtained by considering
only the observations starting at index `r (depth(model)+1)`. We disregard the 
first `r depth(model)` observations. The corresponding log likelihood is

```{r}
loglikelihood(model, initial = "truncated")
```

Using specific contexts amounts to assuming perfect predictions for the
first five observations. The log likelihood is not modified, but it
covers now the full time series, i.e. `r length(California_weeks)`
observations. The number of parameters is increased as
the initial value is now associated to a specific parameter
 leading to a total of
`r (depth(model) + context_number(model))` parameters.

```{r}
loglikelihood(model, initial = "specific")
```

The extended context approach is the most complex. For the first
observation, we use the empty context, i.e. the root of the context
tree. The associated empirical distribution is
$\mathbb{P}(X_1=1)=\frac{1291}{5126+1291}\simeq`r round(1291/(5126+1291),3)`$.
Its contribution to the log likelihood is therefore
$\log \mathbb{P}(X_1=0)\simeq `r round(log(5126/(5126+1291)),3)`$ as the
first observation is equal to `r California_weeks[1]`.

As the root node is not a proper context, the specification of its
associated empirical distribution contributes to the total number of
parameters of the model (i.e. it adds a parameter to the total).

For the second observation, $X_2=`r California_weeks[2]`$, the candidate
context is $0$. However, $0$ is again not a proper context we should
normally look for $X_{0}$ and older values to find a proper context. In
the extended approach, we consider the empirical distribution of values
following a 0 in the time series, which is given by
$\mathbb{P}(X_t=1|X_{t-1}=0)=\frac{940}{4185+940}\simeq`r round(940/(4185+940),3)`$.
The contribution of this observation to the log likelihood is therefore
$\log \mathbb{P}(X_t=0|X_{t-1}=0)\simeq `r round(log(4185/(4185+940)),3)`$. In
addition, this extended context adds again a parameter to the total.

The following observations $X_3$, $X_4$ and $X_5$ have all proper
contexts and contribute in a normal way to the (log) likelihood without
the need for additional parameters. Notice that the number of additional
parameters does not depend on the initial sequence but on the structure
of the context tree. Notice also that while the corresponding nodes are
contexts, they are not the contexts of those three values. Those
contexts cannot be computed due to the lack of older values.

The final value is
```{r}
loglikelihood(model, initial = "extended")
```

## Monotonicity
### Nested models
For a given time series, the candidate VLMCs generated by the context
algorithm follow a nested structure associated to the pruning operation:
the most complete context tree is pruned recursively in order to
generate less and less complex trees. A context in a small tree is also
a suffix of context of a larger tree. The inclusion order is total
unless some cut off values are identical.

A natural expected property of likelihood functions is to observe a
decrease in likelihood for a given time series when switching from a
model $m_1$ to a simpler model $m_2$ provided their parameters are
estimated by maximum likelihood using this time series, in particular
when $m_2$ is nested in $m_1$ in the sense of the likelihood-ratio test.

### Theoretical analysis
Let us consider the case of two VLMC models $m_1$ and $m_2$ where $m_2$
is obtained by pruning $m_1$ (both estimated on
$(x_i)_{1\leq i\leq n}$). Let us first consider $(x_i)_{i>d}$ where $d$
is the order of $m_1$. The contexts of those values are well defined,
both in $m_1$ and $m_2$. The corresponding probabilities can be
factorised according to the contexts as follows (for $m_1$) 
$$
\mathbb{P}_{m_1}(X_{d+1}=x_{d+1},\ldots,X_n=x_n)=\prod_{c\in m_1}\prod_{k,\  ctx(m_1, x_k)=c}\mathbb{P}_{m_1}(X_k=x_k|ctx(m_1, x_k)=c),
$$
where with use $ctx(m_1, x_k)$ to denote the context of $x_k$ in $m_1$ and $c\in m_1$ to
denote all contexts in $m_1$. We have obviously a similar equation for $m_2$.

As $m_2$ is included in $m_1$ we know that each $c\in m_2$ is also the suffix of a context
of $m_1$. When $c$ is a context in both models, they concern the same subset of the time
series and use therefore the same estimated conditional probabilities, leading to
identical values of 
$$\mathbb{P}_{m_1}(X_k=x_k|ctx(m_1, x_k)=c)=\mathbb{P}_{m2}(X_k=x_k|ctx(m_2, x_k)=c).$$ 

When $c$ is the suffix of a context in $m_1$, there is a collection of contexts $c'$ 
for which $c$ is also a suffix. We have then
$$
\{k\mid ctx(m_2, x_k)=c\}=\bigcup_{c'\in m_1, c\text{ is a suffix of }c'}\{j\mid ctx(m_1, x_j)=c'\}.
$$
In words, the collection of observations whose context in $m_1$ has $c$ as a suffix 
is equal to the collection of observations chose context in $m_2$ is $c$. 
Because the conditional probabilities are estimated by maximum likelihood, we have then
$$
\begin{multline*}
\prod_{\{k\mid ctx(m_2, x_k)=c\}}\mathbb{P}_{m_2}(X_k=x_k|ctx(m_2, x_k)=c)\leq\\ \prod_{\{l\mid ctx(m_1, x_k)=c', c\text{ is a suffix of }c'\}}\mathbb{P}_{m_1}(X_l=x_l|ctx(m_1, x_k)=c').
\end{multline*}
$$
Thus overall, as expected, 
$$
\mathbb{P}_{m_2}(X_{d+1}=x_{d+1},\ldots,X_n=x_n)\leq \mathbb{P}_{m_1}(X_{d+1}=x_{d+1},\ldots,X_n=x_n).
$$
However, the models may not have the same order. Fortunately, as probabilities are not larger than 1, 
we have also
$$
\mathbb{P}_{m_2}(X_{d_{m_2}+1}=x_{d_{m_2}+1},\ldots,X_n=x_n)\leq \mathbb{P}_{m_1}(X_{d_{m_1}+1}=x_{d_{m_1}+1},\ldots,X_n=x_n),
$$
where $d_m$ denotes the order of model $m$. 

In conclusion, likelihood functions based on *truncation* or on *specific* contexts are
non increasing when one moves from a VLMC to one of its pruned version. 

The case of *extended* contexts is more complex. Using the hypotheses as above, the
extended likelihood includes approximate contexts for observations $x_{1},\ldots, x_{d_{m_1}}$
and for $x_{1},\ldots, x_{d_{m_2}}$. Let us consider the case where $d_{m_1}=d_{m_2}+1$ 
(without loss of generality). The only difference in the extended likelihoods is then
$\mathbb{P}_{m_1}(X_{d_{m_1}}=x_{d_{m_1}}|ectx(m_1, x_{d_{m_1}}))$ which is computed
using the extended context $ectx(m_1, X_{d_{m_1}})$ and $\mathbb{P}_{m_2}(X_{d_{m_1}}=x_{d_{m_1}}|ctx(m_2, x_{d_{m_1}}))$ which is computed using the true context. 

Notice that 
$$
(x_1,\ldots,x_{d_{m_1}-1},x_{d_{m_1}})=(x_1,\ldots,x_{d_{m_2}-1},x_{d_{m_2}}, x_{d_{m_1}})
$$
Thus one computes the (extended) context of $x_{d_{m_1}}$, the $d_{m_2}$ first
steps are obviously identical in $m_1$ and in $m_2$, as $m_2$ has been obtained
by pruning $m_1$. Depending on the structure of the context tree, it may be possible
possible to determine the true of context of $x_{d_{m_1}}$ in both trees and thus 
the corresponding probabilities will be identical. The only non obvious situation is
when the context of $x_{d_{m_1}}$ cannot be determine in $m_1$. This is only possible
if the context in $m_2$ is of length $d_{m_2}$ and the corresponding leaf was an
internal node in $m_1$. Then we use as the *extended* context in $m_1$ the *normal*
context of $m_2$ and therefore 
$$
\mathbb{P}_{m_1}(X_{d_{m_1}}=x_{d_{m_1}}|ectx(m_1, x_{d_{m_1}}))=\mathbb{P}_{m_2}(X_{d_{m_1}}=x_{d_{m_1}}|ctx(m_2, x_{d_{m_1}})).
$$

Thus in all cases, in the *extended* context interpretation, moving from an extended
context to a true context does not change the probability included in the likelihood. 
Therefore, the extended likelihood is also non increasing with the pruning operation.

### Experimental illustration
We used the saving options of `tune_vlmc()` to keep all the models considered
during the model selection process in the California 
earth quakes analysis. We can use them to illustrate non decreasing behaviour
of the likelihoods. For the *extended* likelikood, we have:
```{r fig.height=4}
CA_models <- c(
  list(California_weeks_earth_quakes_model$saved_models$initial),
  California_weeks_earth_quakes_model$saved_models$all
)
CA_extended <- data.frame(
  cutoff = sapply(CA_models, \(x) x$cutoff),
  loglikelihood = sapply(CA_models, loglikelihood,
    initial = "extended"
  )
)
ggplot(CA_extended, aes(cutoff, loglikelihood)) +
  geom_line() +
  xlab("Cut off (native scale)") +
  ylab("Log likelihood") +
  ggtitle("Extended log likelihood")
```

For the *specific* likelihood we have:

```{r fig.height=4}
CA_specific <- data.frame(
  cutoff = sapply(CA_models, \(x) x$cutoff),
  loglikelihood = sapply(CA_models, loglikelihood,
    initial = "specific"
  )
)
ggplot(CA_specific, aes(cutoff, loglikelihood)) +
  geom_line() +
  xlab("Cut off (native scale)") +
  ylab("Log likelihood") +
  ggtitle("Specific log likelihood")
```
Notice that the time series contains `r length(California_weeks)` observations and 
the maximum order considered by `tune_vlmc()` is `r max(California_weeks_earth_quakes_model$results$depth)`, thus we do not expect
to observe large differences between the different log likelihoods. This is
illustrated on the following figure:
```{r fig.height=4}
CW_combined <- rbind(
  CA_extended[c("cutoff", "loglikelihood")],
  CA_specific[c("cutoff", "loglikelihood")]
)
CW_combined[["Likelihood function"]] <- rep(c("extended", "specific"), times = rep(nrow(California_weeks_earth_quakes_model$results), 2))
ggplot(
  CW_combined,
  aes(cutoff, loglikelihood, color = `Likelihood function`)
) +
  geom_line() +
  xlab("Cut off (native scale)") +
  ylab("Log likelihood") +
  ggtitle("Log likelihood")
```

The case of the *truncated* likelihood is more complex. As noted above, the
numerical values of the *truncated* likelihood are identical to the values of the
*specific* likelihood and thus the above graphical representations are also valid 
for it. However care must be exercised when using the *truncated* likelihood for
model selection, as explained below. 

## Model selection
Optimal (CO)VLMC models are generally selected via penalized likelihood approaches,
with a preference for the BIC, based on its asymptotic consistency. A natural question
is to what extent the different likelihood functions proposed above are adapted
for model selection when combined with a penalty. Notice that consistency results
for the BIC are generally obtained with a *truncated* likelihood function. 

### Specific and extended likelihood
The *specific* and the *extended* likelihood functions do not introduce any obvious
difficulty. In particular, they work with the full data set as they extend the
VLMC model with specific/extended contexts for the initial values. However, they 
tend to penalize complex models more than the *truncated* likelihood. For instance, the
model selected on the California earth quakes is simpler than the one selected
with the *truncated* likelihood (as well as by the *specific* one):
```{r}
CA_model_extented <- tune_vlmc(California_weeks, initial = "extended")
model_extended <- as_vlmc(CA_model_extented)
draw(model_extended, prob = FALSE)
```

This is also the case for, e.g., the sun spot time series used in the introduction
to the package, as shown below:
```{r}
sun_activity <- as.factor(ifelse(sunspot.year >= median(sunspot.year), "high", "low"))
sun_model_tune_truncated <- tune_vlmc(sun_activity, initial = "truncated")
draw(as_vlmc(sun_model_tune_truncated))
```

```{r}
sun_model_tune_extended <- tune_vlmc(sun_activity, initial = "extended")
draw(as_vlmc(sun_model_tune_extended))
```

In this latter case, the *specific* likelihood gives the same results as the
*truncated* one. 

We observe a similar behaviour on a simple second order Markov chain generated
as follows:
```{r}
TM0 <- matrix(c(0.7, 0.3, 0.4, 0.6),
  ncol = 2,
  byrow = TRUE
)
TM1 <- matrix(c(0.4, 0.6, 0.8, 0.2),
  ncol = 2,
  byrow = TRUE
)
init <- c(0, 1)
dts <- c(init, rep(NA, 500))
set.seed(0)
for (i in 3:length(dts)) {
  if (dts[i - 1] == 0) {
    probs <- TM0[dts[i - 2] + 1, ]
  } else {
    probs <- TM1[dts[i - 2] + 1, ]
  }
  dts[i] <- sample(0:1, 1, prob = probs)
}
```

Once again, the *extended* likelihood tends to over penalize "complex" models
```{r}
MC_extended <- tune_vlmc(dts, initial = "extended", save = "all")
draw(as_vlmc(MC_extended))
```
while thishappens neither for the *truncated* likelihood
```{r}
MC_truncated <- tune_vlmc(dts, initial = "truncated", save = "all")
draw(as_vlmc(MC_truncated))
```
no for the *specific* likelihood
```{r}
MC_specific <- tune_vlmc(dts, initial = "specific", save = "all")
draw(as_vlmc(MC_specific))
```

If we increase the number of observations in the synthetic example, to e.g. 5000, 
the three likelihood functions conduct to the same optimal (and true) model, as
expected. On smaller data sets, the use of the *truncated* seem to be more adapted. 

### Truncated likelihood
However, the *truncated* likelihood function is problematic when used 
naively. The difficulty comes from the discarded observations: two VLMC models
with different orders are evaluated on different data sets. If we consider for instance
the BIC  as an approximation of the logarithm of the evidence of the data given 
the model, it is obvious that one cannot compare directly two models on different 
data sets.

A possible solution for model selection based on the *truncated* likelihood 
consists in choosing a maximal order, say $D$ and in evaluating the models only on 
$(x_i)_{D+1\leq i\leq n}$ so that contexts are always computable. This amounts
to additional truncation for simpler models. The solution is used by `tune_vlmc()`
and `tune_covlmc()`. All the examples given above have been constructed using 
this approach. 


## Coherence with other uses of a VLMC
### Sampling
As detailed in `vignette("sampling")` a VLMC model can be used to generate new
discrete time series based on the conditional probability distributions associated
to the contexts. However, raw VLMC models do not specify distributions for the
initial values for which no proper context exists. 

The difficulty is generally circumvented by using a constant initialisation coupled 
with a burn in phase. This is supported by the theoretical results on VLMC bootstrap 
proved by Bühlmann and Wyner in [their seminal paper](https://dx.doi.org/10.1214/aos/1018031204). Indeed the hypothesis used in the
paper ensure an exponential mix-in for Markov Chain and thus the initial values
play essentially no role in the stationary distribution, provided the burn in 
period is "*long enough*". 

In practice, it is difficult to verify that the conditions of the theorem apply and
to turns them into actual numerical values of a *long enough* burn in time. Thus
we use in `simulate.vlmc()` the extended contexts proposed above. This extends the
VLMC into a full model, with specific probability distributions for the initial 
values. This does not prevent e.g. slow mix-in and this does not change the
stationary distribution when it exists, but this gives some coherence between 
the *extended* likelihood function and sampling. Notice that we also support burn 
in period as well as a manual specification of the initial values of a sample. 

### Prediction
A VLMC can also be used for one step ahead prediction of a time series (or even 
for multiple steps ahead), as implement in `predict.vlmc()`. This poses obviously
the same problem as sampling or likelihood calculation for the initial values. We
use in `predict.vlmc()` the extended contexts proposed above, using the same extended
VLMC model principle. This provides full coherence between sampling, likelihood 
calculation and prediction. 

The `metrics.vlmc()` function computes predictive performances of a VLMC on the
time series used to estimate it. Predictions used for those computations are also 
based on the extended context principle. 

