From ab0b1f3cefedb35327a19ec1b6afd560bfdf802d Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Mon, 14 Sep 2015 23:08:02 -0400
Subject: Import supplements and repo reorganization

---
 theory/main.tex   | 397 ------------------------------------------------------
 theory/warmup.tex | 119 ----------------
 2 files changed, 516 deletions(-)
 delete mode 100644 theory/main.tex
 delete mode 100644 theory/warmup.tex

(limited to 'theory')

diff --git a/theory/main.tex b/theory/main.tex
deleted file mode 100644
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--- a/theory/main.tex
+++ /dev/null
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-\documentclass[10pt]{article}
-\usepackage[utf8x]{inputenc}
-\usepackage{amsmath,amsfonts,amsthm}
-\usepackage[english]{babel}
-\usepackage[capitalize, noabbrev]{cleveref}
-\usepackage{algorithm}
-\usepackage{algpseudocode}
-
-\newenvironment{enumerate*}%
-  {\vspace{-2ex} \begin{enumerate} %
-     \setlength{\itemsep}{-1ex} \setlength{\parsep}{0pt}}%
-  {\end{enumerate}}
- 
-\newenvironment{itemize*}%
-  {\vspace{-2ex} \begin{itemize} %
-     \setlength{\itemsep}{-1ex} \setlength{\parsep}{0pt}}%
-  {\end{itemize}}
- 
-\newenvironment{description*}%
-  {\vspace{-2ex} \begin{description} %
-     \setlength{\itemsep}{-1ex} \setlength{\parsep}{0pt}}%
-  {\end{description}}
-
-\DeclareMathOperator{\E}{\mathbb{E}}
-\let\P\relax
-\DeclareMathOperator{\P}{\mathbb{P}}
-\newcommand{\ex}[1]{\E\left[#1\right]}
-\newcommand{\prob}[1]{\P\left[#1\right]}
-\newcommand{\inprod}[1]{\left\langle #1 \right\rangle}
-\newcommand{\R}{\mathbf{R}}
-\newcommand{\N}{\mathbf{N}}
-\newcommand{\C}{\mathcal{C}}
-\newcommand{\eps}{\varepsilon}
-\newcommand{\eqdef}{\mathbin{\stackrel{\rm def}{=}}}
-\newcommand{\llbracket}{[\![}
-
-\newtheorem{theorem}{Theorem}
-\newtheorem{lemma}{Lemma}
-\algrenewcommand\algorithmicensure{\textbf{Output:}}
-\algrenewcommand\algorithmicrequire{\textbf{Input:}}
-\DeclareMathOperator*{\argmax}{arg\,max}
-
-\author{}
-\title{}
-\date{}
-
-\begin{document}
-
-\section{Probabilistic Model}
-During the last decade, a growing body of work has focused on the graph
-inference problem: how can an unknown graph be recovered from the observation
-of cascades propagating along it? (CITE). While our probabilistic model for
-cascades of violence strongly relies on this line of work, we depart from it in
-the following ways:
-\begin{itemize}
-    \item in our case, the graph along which the cascades of violence propagate
-        (the co-offending network) is known, and we are only interested in
-        recovering (and ultimately predicting) the edges of the cascade, that
-        is, identifying who influenced whom.
-    \item the previous models treat multiple cascades as independent
-        observations which occur sequentially. In our case, cascades can die
-        and spawn at all time and temporally overlap. This need to be carefully
-        accounted for in our generative model and the resulting inference
-        algorithms.
-\end{itemize}
-
-\subsection{Pairwise Propagation}
-\label{sec:edge}
-
-For a pair $(u,v)$ of nodes, we model the propagation of the cascade
-from $u$ to $v$ as the sequence of the following two probabilistic events:
-\begin{enumerate}
-
-    \item \emph{structural propagation:} a Bernouilli variable dictates whether
-        or not a propagation occurs. The parameter of this Bernouilli variable
-        is computed from the edge weights of the co-offending network as
-        follows:
-        \begin{equation}\label{eq:structural}
-            p_{u,v}^s \eqdef \prod_{e\in P(u,v)}
-            \frac{1}{1+e^{-\frac{w_e}{\lambda}}}
-        \end{equation}
-        where $P(u,v)$ denotes the shortest path from $u$ to $v$ and $\lambda$
-        is the \emph{structural parameter}. That is, the weight $w_e$ of each
-        edge $e$ on the  path from $u$ to $v$ is mapped to a probability
-        $p_e\in [0,1]$ through the logistic function:
-        \begin{displaymath}
-            p_e \eqdef \frac{1}{1+e^{-\frac{w_e}{\lambda}}}
-        \end{displaymath}
-        Note that the probability of structural propagation both \emph{(a)}
-        decreases as the distance between $u$ and $v$ in the co-offending
-        network increases, and \emph{(b)} increases as the weights of the edges
-        on the path from $u$ to $v$ increase. The structural parameter
-        $\lambda$ controls how quickly the probability $p_e$ of a given edge
-        converges to one as the weight $w_e$ increases.
-
-    \item \emph{temporal propagation:} if the structural propagation occurs,
-        then the propagation time $\Delta_{u,v}$ between $u$ and $v$ is drawn
-        from a continuous distribution with support in $\R_+$. We denote by
-        $f(t)$ the density function of this probability distribution and by
-        $F(t)$ its cumulative function. Table TODO gives the expression of $f$
-        and $F$ for the different temporal distributions considered in this
-        work.
-
-        In our data, the infection times are only observed with a temporal
-        resolution of one day. As a consequence, for two nodes $u$ and $v$
-        whose \emph{observed} infection times are respectively $t_u$ and $t_v$
-        (days), the \emph{real} propagation time could be anything in the
-    interval $(t_v-t_u-1, t_v-t_u]$. Hence, the probability of observing
-    a propagation time $\Delta_{u,v} = t_v - t_u$ is given by:
-    \begin{equation}\label{eq:temporal}
-        p_{u,v}^t(\Delta_{u,v}) \eqdef \int_{\Delta_{u,v}-1}^{\Delta_{u,v}} f(t)dt
-    \end{equation}
-\end{enumerate}
-
-\paragraph{Successful propagation.} For two nodes $u$ and $v$ with observed
-infection times $t_u$ and $t_v$, the probability $p_{u,v}$ that $u$ infected
-$v$ is obtained by multiplying the structural and temporal probabilities given
-by \eqref{eq:structural} and \eqref{eq:temporal}:
-\begin{displaymath}
-    p_{u,v}(t_u, t_v)  \eqdef p_{u,v}^s\cdot p_{u,v}^t(\Delta_{u,v})
-\end{displaymath}
-
-\subsection{Failed Propagations and Spawning of New Cascades}
-\label{sec:roots}
-
-\paragraph{Failed propagation.}
-When an edge fails to propagate the violence cascade, it could be either
-because the structural propagation did not occur, or because the structural
-propagation occurred, but the temporal propagation didn't occur in time.
-
-For an edge $(u,v)$ with infection times $t_u$ and $t_v$, it follows directly
-from Section~\ref{sec:edge} that structural propagation does not occur with
-probability $1-p_{u,v}^s$.
-
-If structural propagation occurs, but temporal propagation does not occur in
-time, it means that node $v$ was infected at time $t_v$ (through other means)
-\emph{before} $u$ succeeds in infecting $v$. That is, the propagation time was
-larger than $\Delta_{u,v}$. This occurs with probability:
-\begin{displaymath}
-    \int_{\Delta_{u,v}}^{+\infty} f(t)dt = 1-F(\Delta_{u,v})
-\end{displaymath}
-
-Overall, the probability of a failed propagation over an edge $(u,v)$ is given
-by:
-\begin{displaymath}
-    \tilde{p}_{u,v}(t_u, t_v)
-    \eqdef 1 - p_{u,v}^s + p_{u,v}^s\left(1-F(\Delta_{u,v})\right)
-    = 1 - p_{u,v}^s F(\Delta_{u,v})
-\end{displaymath}
-
-\paragraph{}For an edge $(u,v)$ where $u$ is a victim node and $v$ is a not
-a victim, the situation is similar: either the structural propagation failed,
-or it succeeded but the temporal propagation did not occur in time. In the
-latter case, since we did not observe an infection at all for $v$, this means
-that the propagation time between $u$ and $v$ was such that the infection time
-of $v$ would have fallen after the end of the study period. Denoting by $T$ the
-observation horizon (the end of the study period), the probability of a failed
-propagation for an edge $(u,v)$ where $v$ is not a victim is:
-\begin{displaymath}
-    \tilde{p}_{u,v}(t_u, T) \eqdef 1-p_{u,v}^s F(T-t_u)
-\end{displaymath}
-
-\paragraph{Spawning of New Cascades.}
-
-Our probabilistic model considers another cause of infection for victim nodes:
-beyond pairwise propagation of the violence cascade, nodes can also
-spontaneously become infected and spawn a new violence cascade. We simply model
-this by a Bernouilli variable of parameter $\beta$: for any node, the
-probability that it becomes infected spontaneously is $\beta$.
-
-\subsection{The Directed Acyclic Vector of Infection}
-\label{sec:dag}
-
-Given a vector of infection times denoted by $\textbf{t}$, it follows from
-Sections~\ref{sec:edge} and \ref{sec:roots} that \emph{(1)} no infection can
-occur between two non victim nodes, \emph{(2)} no infection can occur along an
-edge $(u,v)$ where $t_u > t_v$.
-
-This suggests to orient and trim the co-offending network $G$ to construct the
-following directed graph $D_{\mathbf{t}}$ by applying the following rules:
-\begin{enumerate}
-    \item remove edges between non-victim nodes.
-    \item orient edges from victim nodes to non-victim nodes.
-    \item for any edge $(u,v)$ between two victims, orient it in the direction
-        from smallest infection time to largest infection time.
-\end{enumerate}
-
-The resulting graph $D_{\mathbf{t}}$ only contains edges along with the
-propagation of violence can possibly occur. Furthermore the edges are oriented
-in the direction of propagation.
-
-It has been observed in (CITE) that $D$ is acyclic. For completeness we
-reproduce their proof here and adapt it to account for non-victim nodes (which
-do not exist in the original paper).
-
-\begin{theorem}
-    The graph $D_{\mathbf{t}}$ obtained from $G$ by applying rules 1. to 3. is
-    acyclic.
-\end{theorem}
-
-\begin{proof}
-    Assume by contradiction that $D$ contains a cycle $(u_1,\ldots, u_n)$ where
-    $(u_1, u_2),\dots,(u_{n-1}, u_n), (u_n, u_1)$ are edges of $D$. Because of
-    rule 1., all nodes $u_1,\dots, u_n$ are victim nodes.
-
-    Applying rule 3. to all edges in the cycle implies that
-    $t_1<t_2<\dots<t_n<t_1$. The two extremal terms of this chain of
-    inequalities implie $t_1<t_1$ which is a contradiction.
-\end{proof}
-
-\subsection{Cascade Model}
-
-Using Sections~\ref{sec:edge} to \ref{sec:dag}, we can now fully specify our
-cascade model.
-
-The violence propagation proceeds as follows: every time a node becomes
-infected, it attempts to infect all its neighbors which are not infected yet
-according to the pairwise model of Section~\ref{sec:edge}. In other words, at
-a given time, a node is subjected to the ongoing infection attempts of all its
-neighbors which have been infected in the past. Because of the temporal
-component of the pairwise propagation probabilities, these attempts will
-succeed at different times. The infection is attributed to the first neighbor
-who succeeds.Furthermore, all the infection attempts are independent from each
-other.  
-
-\paragraph{Example.} Let us consider a node $v$ with $n$ neighbors $u_1,\dots,
-u_n$. For simplicity, let us assume that the structural propagation
-probabilities are the same and equal to one for all edges $(u_i, v)$. We also
-assume that all neighbors of $v$ were infected at time $0$ and we use the
-exponential temporal model with parameter $\alpha$. Then, the probability that
-$v$ is infected at time larger than $t$ is given by:
-\begin{displaymath}
-    p_v(t) \eqdef \P\left[\min_{1\leq i\leq n} Z_i \geq t\right]
-    = e^{-n\alpha t}
-\end{displaymath}
-where $Z_i$ is an exponential variable of parameter $\alpha$ and where we used
-that the law of the minimum of exponential variables is also exponential with
-parameter equal to the sum of the individual variables' parameters.
-
-This example shows that the larger the neighborhood the more likely it is that
-a node will be infected quickly. The parameter $\alpha n$ of the resulting
-exponential variable captures the aggregated infection potential of the
-neighborhood.
-
-\paragraph{} Since the infection of a node $v$ is always attributed to the
-first neighbor which succeeds in infecting it, a victim node
-can have either one parent (when it is infected by it) or no parent (when it
-spawns a cascade spontaneously). Because the graph $D$ along which the cascades
-of violence occur is acyclic, it follows that the violence propagates along $D$
-by forming trees.
-
-Formally, if we denote by $(V, E)$ the vertices and edges of $D$, and we write
-$V = R\cup T$ where $R$ are the victim nodes and $T$ the non-victim nodes
-($R\cap T=\emptyset$), then \emph{the violence cascades form a random forest
-spanning $R$ in $D$}.
-
-\paragraph{Likelihood.} Given pairwise parameters $(\alpha, \lambda, \beta)$
-and a random forest $\mathcal{T} = (T_1,\ldots, T_r)$, we can compute the
-probability of observing the infection times given by the vector $\textbf{t}$:
-\begin{equation}
-    \label{eq:model}
-    \P[\mathbf{t}, \mathcal{T}\,|\, \beta, \alpha, \lambda]
-    = \beta^r(1-\beta)^{n-r}\prod_{e\in E_{\mathcal{T}}} p_e
-    \prod_{e\in E_{D_{\mathcal{\mathbf{t}}}}\setminus E_{\mathcal{T}}}
-    \tilde{p}_e
-\end{equation}
-where $E_{\mathcal{T}}$ denotes the edge set of the forest $\mathcal{T}$ and
-$E_{D_{\mathbf{t}}}$ denotes the edge set of the DAG $D_{\mathbf{t}}$ defined
-in Section~\ref{sec:dag}.
-
-\section{Maximum Likelihood Inference}
-
-The probabilistic model \eqref{eq:model} defines the joint probability of
-a pair $\mathbf{t}, \mathcal{T}$ of infection times and cascades. However, we
-are interested in situations where only the infection times are observed.
-
-A direct application of maximum likelihood estimation would recover the
-parameters $\beta, \alpha$ and $\lambda$ by solving the following optimization
-program:
-\begin{displaymath}
-    \max_{\alpha,\beta,\lambda}\sum_{\mathcal{T}}
-    \P[\mathbf{t}, \mathcal{T}\,|\, \beta, \alpha, \lambda]
-\end{displaymath}
-However, since we are also interested in recovering the infection edges, we
-solve the following optimization program instead:
-\begin{equation}
-    \label{eq:program}
-    \mathcal{T}^*, \beta^*, \alpha^*, \lambda^* \in
-    \argmax_{\mathcal{T},\beta,\alpha,\lambda}
-    \P[\mathbf{t}, \mathcal{T}\,|\, \beta, \alpha, \lambda]
-\end{equation}
-When solving this optimization program, the optimal forest $\mathcal{T}^*$ will
-indicate the most likely edges along which the infection could have propagated,
-while $\beta$, $\alpha$ and $\lambda$ will give us information about the
-temporal and structural scale of pairwise infection.
-
-\subsection{Finding the Cascades}
-
-We first assume that $\beta$, $\alpha$ and $\lambda$ are fixed and solve
-\eqref{eq:program} for $\mathcal{T}$:
-\begin{equation}\label{eq:program2}
-    \mathcal{T}^*\in\argmax_{\mathcal{T}}
-    \beta^r(1-\beta)^{n-r}\prod_{e\in E_{\mathcal{T}}} p_e
-    \prod_{e\in E_{D_{\mathcal{\mathbf{t}}}}\setminus E_{\mathcal{T}}}
-    \tilde{p}_e
-\end{equation}
-Note that the solution $\mathcal{T}^*$ depends on the value of $(\beta, \alpha,
-\lambda)$. We will then optimize over these parameters in the following
-sections.
-
-By multiplying and dividing each term by $\tilde{p}_e$ in the first product, we
-can rewrite the objective function:
-\begin{displaymath}
-    \mathcal{T}^*\in\argmax_{\mathcal{T}}
-    \left(\frac{\beta}{1-\beta}\right)^r(1-\beta)^n
-    \prod_{e\in E_{\mathcal{T}}} \frac{p_e}{\tilde{p}_e}
-    \prod_{e\in E_{D_{\mathcal{\mathbf{t}}}}} \tilde{p}_e
-\end{displaymath}
-Note that now, the last product, as well as the $(1-\beta)^n$ do not depend on
-the choice of $\mathcal{T}$ and can be dropped without changing the
-optimization program. Then, by taking the $\log$, we obtain the following
-equivalent optimization program:
-\begin{displaymath}
-    \mathcal{T}^*\in\argmax_{\mathcal{T}}\; r\log\frac{\beta}{1-\beta}
-    +\sum_{e\in E_{\mathcal{T}}} \log\frac{p_e}{\tilde{p}_e}
-\end{displaymath}
-
-As mentioned in Section XXX, $\mathcal{T}$ is a random forest spawning $R$, the
-set of victim nodes, in $D_{\mathbf{t}}$. So choosing $\mathcal{T}$ amounts to
-choosing for each node in $R$:
-\begin{itemize}
-    \item either to make it the root of a new tree, in which case it will
-        contribute a term $\log\frac{\beta}{1-\beta}$ to the objective
-        function.
-    \item either to make it part of an existing tree, by including one of its
-        incoming edge $e$ in the edge set $E_{\mathcal{T}}$. In this case, the
-        node contributes a term $\log\frac{p_e}{\tilde{p}_e}$ to the objective
-        function.
-\end{itemize}
-
-Since our goal is to solve \eqref{eq:program2}, in the second case, it is
-sufficient to consider the incoming edge which maximizes the quantity
-$\frac{p_e}{\tilde{p}_e}$. For this edge $e$, deciding between the two cases
-then amounts to comparing $\frac{p_e}{\tilde{p}_e}$ to $\frac{\beta}{1-\beta}$.
-This leads to a simple thresholding algorithm for solving \eqref{eq:program2}. 
-
-\begin{algorithm}
-    \caption{Thresholding algorithm for cascade recovery}
-    \begin{algorithmic}[1]
-    \Require graph $G$, infection times $\mathbf{t}$, $\alpha$, $\beta$, $\lambda$
-    \Ensure cascades $\mathcal{T}$
-    \State $D_{\mathbf{t}}\gets$ acyclic graph obtained by trimming $G$
-    \State $R\gets\{u\in G: t_u < \infty\}$
-    \State $E\gets\emptyset$
-    \For{$v\in R$}
-    \State $u\gets\argmax\left\{\frac{p_{u,v}}{\tilde{p}_{u,v}}: (u,v)\in
-    D_t\right\}$
-    \If{$\frac{p_{u,v}}{\tilde{p}_{u,v}}\geq \frac{\beta}{1-\beta}$}
-    \State $E\gets E\cup \{(u,v)\}$
-    \EndIf
-    \EndFor
-    \State \textbf{return} $\mathcal{T} = (R, E)$
-    \end{algorithmic}
-\end{algorithm}
-
-\subsection{Finding the Spawning Parameter}
-
-In this section, we consider the value of $\alpha$ and $\lambda$ fixed and try
-to optimize \eqref{eq:program} jointly over $\mathcal{T}$ and $\beta$.
-
-For a node $v\in R$, let us define:
-\begin{displaymath}
-    q_v \eqdef \max\left\{\frac{p_{u,v}}{\tilde{p}_{u,v}}:(u,v)\in E_{\mathbf{t}}\right\}
-\end{displaymath}
-and denote by $E_{\beta}$ the set:
-\begin{displaymath}
-    R_{\beta} \eqdef \left\{v\in R:q_v\geq \frac{\beta}{1-\beta}\right\}
-\end{displaymath}
-Then it follows from the analysis of Section XXX, that for a fixed value of
-$\beta$, the optimal value of \eqref{eq:program} when optimizing over
-$\mathcal{T}$ is given by:
-\begin{displaymath}
-    f(\beta) = (|R|-|R_\beta|)\log\frac{\beta}{1-\beta}
-    + n\log(1-\beta)+ \sum_{v\in R_\beta} \log q_v
-\end{displaymath}
-Hence our goal is to find the maximum of $f(\beta)$ over the interval $(0,1)$.
-Note that the maximum exists since $f$ diverges to $-\infty$ as $\beta$
-approaches $0$ or $1$. Furthermore $f$ is continuous over $(0,1)$ since we can
-rewrite it:
-\begin{displaymath}
-    f(\beta) = n\log(1-\beta) + \sum_{v\in R}
-    \log\max\left\{\frac{\beta}{1-\beta}, q_v\right\}
-\end{displaymath}
-
-\subsection{Finding the Pairwise Propagation Parameters}
-
-\end{document}
diff --git a/theory/warmup.tex b/theory/warmup.tex
deleted file mode 100644
index c34eb89..0000000
--- a/theory/warmup.tex
+++ /dev/null
@@ -1,119 +0,0 @@
-\documentclass[10pt]{article}
-\usepackage[utf8x]{inputenc}
-\usepackage{amsmath,amsfonts,amsthm}
-\usepackage[english]{babel}
-\usepackage[capitalize, noabbrev]{cleveref}
-\usepackage{algorithm}
-\usepackage{algpseudocode}
-
-\newenvironment{enumerate*}%
-  {\vspace{-2ex} \begin{enumerate} %
-     \setlength{\itemsep}{-1ex} \setlength{\parsep}{0pt}}%
-  {\end{enumerate}}
- 
-\newenvironment{itemize*}%
-  {\vspace{-2ex} \begin{itemize} %
-     \setlength{\itemsep}{-1ex} \setlength{\parsep}{0pt}}%
-  {\end{itemize}}
- 
-\newenvironment{description*}%
-  {\vspace{-2ex} \begin{description} %
-     \setlength{\itemsep}{-1ex} \setlength{\parsep}{0pt}}%
-  {\end{description}}
-
-\DeclareMathOperator{\E}{\mathbb{E}}
-\let\P\relax
-\DeclareMathOperator{\P}{\mathbb{P}}
-\newcommand{\ex}[1]{\E\left[#1\right]}
-\newcommand{\prob}[1]{\P\left[#1\right]}
-\newcommand{\inprod}[1]{\left\langle #1 \right\rangle}
-\newcommand{\R}{\mathbf{R}}
-\newcommand{\N}{\mathbf{N}}
-\newcommand{\C}{\mathcal{C}}
-\newcommand{\eps}{\varepsilon}
-\newcommand{\eqdef}{\mathbin{\stackrel{\rm def}{=}}}
-\newcommand{\llbracket}{[\![}
-
-\newtheorem{theorem}{Theorem}
-\newtheorem{lemma}{Lemma}
-\algrenewcommand\algorithmicensure{\textbf{Output:}}
-\algrenewcommand\algorithmicrequire{\textbf{Input:}}
-\DeclareMathOperator*{\argmax}{arg\,max}
-
-\author{}
-\title{}
-\date{}
-
-\begin{document}
-
-\paragraph{Notations}
-\begin{itemize}
-    \item $n$ criminals, $V = \{1,\dots,n\}$
-    \item $\alpha_{i,j}$ influence of criminal $i$ on criminal $j$.
-    \item the infections form a set of pairs $C = \{(t_i, v_i),\; 1\leq i\leq m\}$
-        where $t_i$ is the time and $v_i\in\{1,\dots, n\}$ is the identifier of
-        the criminal.
-\end{itemize}
-
-\paragraph{Model}
-The infections are modeled as a multivariate Hawkes process. The instantaneous rate
-of infection of user $j$ at time $t$ is given by:
-\begin{displaymath}
-    \lambda_j(t) = \lambda(t) + \sum_{t_i<t}\alpha_{v_i, j}
-    \phi(t-t_i)
-\end{displaymath}
-
-\emph{Technical concern:} stability of the process, there is a condition on the
-$\alpha_{i,j}$ parameters so that the process is stable, i.e does not keep
-accelerating forever. We probably do not need to worry about it too much:
-empirically the process is stable.
-
-\paragraph{Time kernel}
-we choose an exponential kernel for $\phi$ (will allow the recursive trick
-later on) $ \phi(t) = \mu e^{-\mu t} $
-
-\paragraph{Base rate} $\lambda(t)$ captures the seasonal effect. TBD, worst
-case is to choose a constant approximation, $\lambda(t) = \lambda_0$. We
-define $\Lambda(T) \eqdef \int_{0}^T \lambda(t)dt$.
-
-\paragraph{Edge model} TBD
-
-\paragraph{Likelihood} The log-likelihood for observation horizon $T$ is given
-by:
-\begin{displaymath}
-    \mathcal{L}(C) = \sum_{i=1}^m \log \lambda_{v_i}(t_i)
-    - \sum_{j=1}^n\int_{0}^T \lambda_j(t)dt
-\end{displaymath}
-Note that we have:
-\begin{displaymath}
-    \int_{0}^T \lambda_j(t)dt = \int_{0}^T\lambda(t)dt + \sum_{i=1}^m
-        \alpha_{v_i, j}\big[1-e^{-\mu(T-t_i)}\big]
-\end{displaymath}
-
-Hence the log-likelihood can be rewritten (after reordering a little bit):
-\begin{displaymath}
-    \mathcal{L}(C) = \sum_{i=1}^m\log \lambda_{v_i}(t_i)
-    - \sum_{i=1}^m A_{v_i} \big[1-e^{-\mu(T-t_i)}\big]
-    - n\Lambda(T)
-\end{displaymath}
-where $A_{v_i} = \sum_{j=1}^n \alpha_{v_i, j}$ (out-weight of criminal $v_i$),
-or in complete form:
-\begin{displaymath}
-    \mathcal{L}(C) = \sum_{i=1}^m\log\left[ \lambda(t_i)
-    + \sum_{j=1}^{i-1}\alpha_{v_j, v_i}\mu e^{-\mu(t_i-t_j)}\right]
-    - \sum_{i=1}^m A_{v_i} \big[1-e^{-\mu(T-t_i)}\big]
-    - n\Lambda(T)
-\end{displaymath}
-
-\paragraph{Computational trick} As written above, the log-likelihood can be
-computed in time $O(m^2 + n^2)$. This is not linear in the input size, which is
-$O(m + n^2)$. The $O(m^2)$ term comes from the first sum in the definition of
-the log-likelihood. However, in the case of an exponential time kernel, using
-a recursive trick, the complexity of this term can be reduced to $O(mn)$ for an
-overall running time of $O(mn + n^2)$.
-
-\emph{Question:} Is it worth it in our case? Yes if the number of victims
-is significantly smaller than the number of infections. Is it the case?
-
-
-\end{document}
-- 
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