---
title: "Markov-modulated (marked) Poisson processes"
author: "Jan-Ole Koslik"
output: rmarkdown::html_vignette
# output: pdf_document
vignette: >
  %\VignetteIndexEntry{MMMPPs}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
bibliography: refs.bib
link-citations: yes
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  # fig.path = "img/",
  fig.align = "center",
  fig.dim = c(8, 6),
  out.width = "85%"
)
```

The functionalities of `LaMa` can also be called to fit so-called **Markov-modulated Poisson processes**. These are doubly stochastic Poisson point processes where the intensity is directed by an underlying continuous-time Markov chain. Such processes are useful for modeling arrival times, for example of calls in a call center, or patients in the hospital. The main difference compared to continuous-time HMMs is that we explicitely model the arrival times as random-variables, i.e. we assign probabilities to them. A homogeneous Poisson process is mainly characterized by the fact that the number of arrivals in a fixed time interval is Poisson distributed with a mean that is proporional to the length of the interval. The waiting times between arrivals are exponentially distributed. While the latter is not true for non-homogeneous Poisson processes in general, we can interpret a Markov modulated Poisson process as alternating between homogeneous Poisson processes, i.e. when the unobserved continuous-time Markov chain stays in a particular state for some interval, the associated Poisson rate in that interval is homogeneous and state-specific. To learn more about Poisson processes, see @dobrow2016introduction.

## Example 1: Markov-modulated Poisson processes

```{r, setup}
# loading the package
library("LaMa")
```


### Setting parameters 

We choose to have a considerably higher rate and shorter stays of the underlying Markov chain in state 2, i.e. state 2 is **bursty**.

```{r, parameters}
# state-dependent rates
lambda = c(2, 15)
# generator matrix of the underlying Markov chain
Q = matrix(c(-0.5,0.5,2,-2), nrow = 2, byrow = TRUE)
```

### Simulating an MMPP
```{r, simulation}
set.seed(123)

k = 200 # number of state switches
trans_times = s = rep(NA, k) # time points where the chain transitions
s[1] = sample(1:2, 1) # initial distribuion c(0.5, 0.5)
# exponentially distributed waiting times
trans_times[1] = rexp(1, -Q[s[1],s[1]])
# in a fixed interval, the number of arrivals is Pois(lambda * interval_length)
n_arrivals = rpois(1, lambda[s[1]]*trans_times[1]) 
# arrival times within fixed interval are uniformly distributed
arrival_times = runif(n_arrivals, 0, trans_times[1])
for(t in 2:k){
  s[t] = c(1,2)[-s[t-1]] # for 2-states, always a state swith when transitioning
  # exponentially distributed waiting times
  trans_times[t] = trans_times[t-1] + rexp(1, -Q[s[t], s[t]])
  # in a fixed interval, the number of arrivals is Pois(lambda * interval_length)
  n_arrivals = rpois(1, lambda[s[t]]*(trans_times[t]-trans_times[t-1]))
  # arrival times within fixed interval are uniformly distributed
  arrival_times = c(arrival_times, 
                    runif(n_arrivals, trans_times[t-1], trans_times[t]))
}
arrival_times = sort(arrival_times)
```

Let's visualize the simulated MMPP

```{r, vis_MMPP}
n = length(arrival_times)
color = c("orange", "deepskyblue")
plot(arrival_times[1:100], rep(0.5,100), type = "h", bty = "n", ylim = c(0,1), 
     yaxt = "n", xlab = "arrival times", ylab = "")
segments(x0 = c(0,trans_times[1:98]), x1 = trans_times[1:99], 
         y0 = rep(0,100), y1 = rep(0,100), col = color[s[1:99]], lwd = 4)
legend("top", lwd = 2, col = color, legend = c("state 1", "state 2"), box.lwd = 0)
```
What makes the MMPP special compared to a regular Poisson point process is its **burstiness** when the Markov chain is in the second state.

### Writing the negative log-likelihood function

The likelihood of a stationary MMPP for waiting times $x_1, \dots, x_n$ is (@meier1987fitting, @langrock2013markov)
$$
L(\theta) = \delta \bigl(\prod_{i=1}^n \exp((Q-\Lambda)x_i)\Lambda\bigr)1,
$$
where $Q$ is the generator matrix of the continuous-time Markov chain, $\Lambda$ is a diagonal matrix of state-dependent Poisson intensities, $\delta$ is the stationary distribution of the continuous-time Markov chain, and $1$ is a column vector of ones. For more details on continuous-time Markov chains, see the vignette *continuous-time HMMs* or also @dobrow2016introduction.
We can easily calculate the log of the above expression using the standard implementation of the general forward algorithm `forward_g()` when choosing the first matrix of state-dependent densities to be the identity (i.e.) the first row of the `allprobs` matrix to be one and all other matrices of state-dependent density matrices to be $\Lambda$.

```{r, mllk}
mllk = function(theta.star, timediff, N=2){
  lambda = exp(theta.star[1:N]) # state specific rates
  Q = diag(N) # generator matrix
  Q[!Q] = exp(theta.star[N+1:(N*(N-1))])
  diag(Q) = 0
  diag(Q) = -rowSums(Q)
  Qube = LaMa::tpm_cont(Q-diag(lambda), timediff) # exp((Q-Lambda)*deltat)
  allprobs = matrix(lambda, nrow = length(timediff+1), ncol = N, byrow = T)
  allprobs[1,] = 1
  delta = solve(t(Q+1), rep(1,N), tol = 1e-20)
  -LaMa::forward_g(delta, Qube, allprobs)
}
```

### Fitting an MMPP to the data
```{r, model, warning=FALSE}
theta.star = log(c(2, 15, # lambda
                   2, 0.5)) # off-diagonals of Q

timediff = diff(arrival_times)

t1 = Sys.time()
mod = nlm(mllk, theta.star, timediff=timediff, stepmax = 10)
# we often need the stepmax, as the matrix exponential can be numerically unstable
Sys.time()-t1
```

### Results

```{r, results}
exp(mod$estimate)
```

## Example 2: Markov-modulated marked Poisson processes

Such processes can also carry additional information, so called **marks**, at every arrival time when we also observe the realization of a different random variable that only depends on the underlying states of the continuous-time Markov chain. For example for patient arrivals in the hospital we could observe a biomarker at every arrival time. Information on the underlying health status is then present in both, the arrival times (because sick patients visit more often) and the biomarkers.

```{r, parameters2}
# state-dependent rates
lambda = c(1, 5, 20)
# generator matrix of the underlying Markov chain
Q = matrix(c(-0.5,0.3,0.2,
             0.7, -1, 0.3,
             1 ,1,-2), nrow = 3, byrow = TRUE)
# parmeters for distributions of state-dependent marks
# (here normally distributed)
mu = c(-5, 0, 5)
sigma = c(2, 1, 2)

color = c("orange", "deepskyblue", "seagreen2")
curve(dnorm(x, 0, 1), xlim = c(-10,10), bty = "n", lwd = 2, col = color[2], 
      n = 200, ylab = "density", xlab = "mark")
curve(dnorm(x, -5, 2), add = TRUE, lwd = 2, col = color[1], n = 200)
curve(dnorm(x, 5, 2), add = TRUE, lwd = 2, col = color[3], n = 200)
```

### Simulating an MMMPP

We now show how to simulate an MMMPP and additionally how to generalize to more than two hidden states.

```{r, simulation2}
set.seed(123)
k = 200 # number of state switches
trans_times = s = rep(NA, k) # time points where the chain transitions
s[1] = sample(1:3, 1) # initial distribuion uniformly
# exponentially distributed waiting times
trans_times[1] = rexp(1, -Q[s[1],s[1]])
# in a fixed interval, the number of arrivals is Pois(lambda * interval_length)
n_arrivals = rpois(1, lambda[s[1]]*trans_times[1]) 
# arrival times within fixed interval are uniformly distributed
arrival_times = runif(n_arrivals, 0, trans_times[1])
# marks are iid in interval, given underlying state
marks = rnorm(n_arrivals, mu[s[1]], sigma[s[1]])

for(t in 2:k){
  # off-diagonal elements of the s[t-1] row of Q divided by the diagonal element
  # give the probabilites of the next state
  s[t] = sample(c(1:3)[-s[t-1]], 1, prob = Q[s[t-1],-s[t-1]]/-Q[s[t-1],s[t-1]])
  # exponentially distributed waiting times
  trans_times[t] = trans_times[t-1] + rexp(1, -Q[s[t],s[t]])
  # in a fixed interval, the number of arrivals is Pois(lambda * interval_length)
  n_arrivals = rpois(1, lambda[s[t]]*(trans_times[t]-trans_times[t-1]))
  # arrival times within fixed interval are uniformly distributed
  arrival_times = c(arrival_times, 
                    runif(n_arrivals, trans_times[t-1], trans_times[t]))
  # marks are iid in interval, given underlying state
  marks = c(marks, rnorm(n_arrivals, mu[s[t]], sigma[s[t]]))
}
arrival_times = sort(arrival_times)
```


Let's visualize the simulated MM**M**PP

```{r, vis_MMMPP}
n = length(arrival_times)
plot(arrival_times[1:100], marks[1:100], pch = 16, bty = "n", 
     ylim = c(-9,9), xlab = "arrival times", ylab = "marks")
segments(x0 = c(0,trans_times[1:98]), x1 = trans_times[1:99], 
         y0 = rep(-9,100), y1 = rep(-9,100), col = color[s[1:99]], lwd = 4)
legend("topright", lwd = 2, col = color, 
       legend = c("state 1", "state 2", "state 3"), box.lwd = 0)
```

### Writing the negative log-likelihood function

The likelihood of a stationary MM**M**PP for waiting times $x_1, \dots, x_n$ between marks $y_0, y_1, \dotsc, y_n$ only changes slightly from the MMPP likelihood, as we include the matrix of state-specific densities (@lu2012markov, @mews2023markov):
$$
L(\theta) = \delta P(y_0) \bigl(\prod_{i=1}^n \exp((Q-\Lambda) x_i)\Lambda P(y_i) \bigr)1,
$$
where $Q$, $\Lambda$ and $\delta$ are as above and $P(y_i)$ is a diagonal matrix with state-dependent densites for the observation at time $t_i$. We can again easily calculate the log of the above expression using the standard implementation of the general forward algorithm `forward_g()` when first calculating the `allprobs` matrix with state-dependent densities for the marks (as usual for HMMs) and then multiplying each row except the first one element-wise with the state-dependent rates.

```{r, mllk2}
mllk = function(theta.star, y, timediff, N){
  lambda = exp(theta.star[1:N]) # state specific rates
  mu = theta.star[N+1:N]
  sigma = exp(theta.star[2*N+1:N])
  Q = diag(N) # generator matrix
  Q[!Q] = exp(theta.star[3*N+1:(N*(N-1))])
  diag(Q) = 0
  diag(Q) = -rowSums(Q)
  delta = solve(t(Q+1), rep(1,N), tol = 1e-20)
  Qube = LaMa::tpm_cont(Q-diag(lambda), timediff) # exp((Q-Lambda)*deltat)
  allprobs = matrix(1, length(y), N)
  for(j in 1:N){
    allprobs[,j] = dnorm(y, mu[j], sigma[j])
  }
  allprobs[-1,] = allprobs[-1,] * matrix(lambda, length(y)-1, N, byrow = T)
  -LaMa::forward_g(delta, Qube, allprobs)
}
```

### Fitting an MMPP to the data

```{r, model2, warning=FALSE}
theta.star = c(log(c(1, 5, 20)), # lambda
                   -5, 0, 5, # mu
               log(c(2, 1, 2)), # sigma
               log(c(0.7, 1, 0.3, 1, 0.2, 0.3))) # Q
timediff = diff(arrival_times)
t1 = Sys.time()
mod2 = nlm(mllk, theta.star, y = marks, timediff=timediff, N=3, stepmax = 5)
Sys.time()-t1
```

### Results

```{r, results2}
N = 3
round(exp(mod2$estimate[1:N]),2)
# mu
round(mod2$estimate[N+1:N], 2)
# sigma
round(exp(mod2$estimate[2*N+1:N]), 2)
Q = diag(N)
Q[!Q] = exp(mod2$estimate[3*N+1:(N*(N-1))])
diag(Q) = 0
diag(Q) = -rowSums(Q)
round(Q, 3)
```

## References