User:Timothee Flutre/Notebook/Postdoc/2011/12/14

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Learn about mixture models and the EM algorithm (Caution, this is my own quick-and-dirty tutorial, see the references at the end for presentations by professional statisticians.) Motivation: a large part of any scientific activity is about measuring things, in other words collecting data, and it is not infrequent to collect heterogeneous data. It seems therefore natural to say that the samples come from a mixture of clusters. The aim is thus to recover from the data, ie. to infer, (i) how many clusters there are, (ii) what are the features of these clusters, and (iii) from which cluster each sample comes from. In the following, I will focus on points (ii) and (iii). Data: we have N observations, noted [math]\displaystyle{ X = (x_1, x_2, ..., x_N) }[/math]. For the moment, we suppose that each observation [math]\displaystyle{ x_i }[/math] is univariate, ie. each corresponds to only one number. Assumption: let's assume that the data are heterogeneous and that they can be partitioned into [math]\displaystyle{ K }[/math] clusters (in this document, we suppose that [math]\displaystyle{ K }[/math] is known). This means that we expect a subset of the observations to come from cluster [math]\displaystyle{ k=1 }[/math], another subset to come from cluster [math]\displaystyle{ k=2 }[/math], and so on. Model: writing down the model usually means starting by writing down the likelihood of the parameters, that is the probability of the data given the parameters. Technically, we say that the observations were generated according to a density function [math]\displaystyle{ f }[/math]. In this case, this density is itself a mixture of densities, one per cluster. In our case, we will assume that observations from cluster [math]\displaystyle{ k }[/math] are generated from a Normal distribution, which density is here noted [math]\displaystyle{ \phi }[/math], with mean [math]\displaystyle{ \mu_k }[/math] and standard deviation [math]\displaystyle{ \sigma_k }[/math]. Moreover, as we don't know for sure from which cluster a given observation comes from, we define the mixture weight [math]\displaystyle{ w_k }[/math] (also called mixing proportion) to be the probability that any given observation comes from cluster [math]\displaystyle{ k }[/math]. As a result, we have the following list of parameters: [math]\displaystyle{ \theta=(w_1,...,w_K,\mu_1,...\mu_K,\sigma_1,...,\sigma_K) }[/math]. Finally, for a given observation [math]\displaystyle{ x_i }[/math], we can write the model: [math]\displaystyle{ f(x_i|\theta) = \sum_{k=1}^{K} w_k \, \phi(x_i|\mu_k,\sigma_k) = \sum_{k=1}^{K} w_k \frac{1}{\sigma_k \sqrt{2\pi}} \exp \left( -\frac{1}{2} \left( \frac{x_i - \mu_k}{\sigma_k} \right) ^2 \right) }[/math] The constraints are: [math]\displaystyle{ \forall k, w_k \ge 0 }[/math] and [math]\displaystyle{ \sum_{k=1}^K w_k = 1 }[/math] Maximum-likelihood: naturally, we can start by maximizing the likelihood in order to estimate the parameters: [math]\displaystyle{ L(\theta) = P(X|\theta) = \prod_{i=1}^N f(x_i|\theta) = \prod_{i=1}^N \sum_{k=1}^K w_k \phi(x_i;\theta_k) }[/math] Note that, to simply calculate this likelihood, we need to calculate [math]\displaystyle{ K^N }[/math] terms, which is quickly too costly. As usual, it's easier to deal with the log-likelihood: [math]\displaystyle{ l(\theta) = \sum_{i=1}^N ln \left( \sum_{k=1}^K w_k \phi(x_i; \theta_k) \right) }[/math] Let's take the derivative with respect to one parameter, eg. [math]\displaystyle{ \theta_l }[/math]: [math]\displaystyle{ \frac{\partial l}{\partial \theta_l} = \sum_{i=1}^N \frac{1}{\sum_{k=1}^K w_k \phi(x_i; \theta_k)} w_l \frac{\partial \phi(x_i; \theta_l)}{\partial \theta_l} }[/math] [math]\displaystyle{ \frac{\partial l}{\partial \theta_l} = \sum_{i=1}^N \frac{w_l \phi(x_i; \theta_l)}{\sum_{k=1}^K w_k \phi(x_i; \theta_k)} \frac{1}{\phi(x_i; \theta_l)} \frac{\partial \phi(x_i; \theta_l)}{\partial \theta_l} }[/math] [math]\displaystyle{ \frac{\partial l}{\partial \theta_l} = \sum_{i=1}^N \frac{w_l \phi(x_i; \theta_l)}{\sum_{k=1}^K w_k \phi(x_i; \theta_k)} \frac{\partial ln ( \phi(x_i; \theta_l) )}{\partial \theta_l} }[/math] This shows that maximizing the likelihood of a mixture model is like doing a weighted likelihood maximization. However, these weights depend on the parameters we want to estimate! That's why we now switch to the missing-data formulation of the mixture model. Missing data: we introduce the following N latent variables [math]\displaystyle{ Z_1,...,Z_i,...,Z_N }[/math] (also called hidden or allocation variables), one for each observation, such that [math]\displaystyle{ Z_i=k }[/math] means that observation [math]\displaystyle{ x_i }[/math] belongs to cluster [math]\displaystyle{ k }[/math]. Thanks to this, we can reinterpret the mixture weights: [math]\displaystyle{ \forall i, P(Z_i=k|\theta)=w_k }[/math]. In fact, it is much easier to do the maths when defining each [math]\displaystyle{ Z_i }[/math] as a vector of length [math]\displaystyle{ K }[/math], with [math]\displaystyle{ Z_{ik}=1 }[/math] if observation [math]\displaystyle{ x_i }[/math] belongs to cluster [math]\displaystyle{ k }[/math], and [math]\displaystyle{ Z_{ik}=0 }[/math] otherwise (indicator variables). Moreover, we can now define the membership probabilities, one for each observation: [math]\displaystyle{ p(k|i) = P(Z_{ik}=1|x_i,\theta) = \frac{P(Z_{ik}=1 | \theta) p(x_i | Z_{ik}=1,\theta)}{p(x_i | \theta)} = \frac{w_k \phi(x_i|\mu_k,\sigma_k)}{\sum_{l=1}^K w_l \phi(x_i|\mu_l,\sigma_l)} }[/math] The observed-data likelihood (also called sometimes "incomplete" or "marginal", even though these appellations are misnomers) is still written the same way: [math]\displaystyle{ L_{obs}(\theta) = P(X|\theta) = \prod_{i=1}^N f(x_i|\theta) }[/math] But now we can also write the augmented-data likelihood (also called sometimes "complete"), assuming all observations are independent conditionally on their membership: [math]\displaystyle{ L_{aug}(\theta) = P(X,Z|\theta) = \prod_{i=1}^N P(x_i|Z_i,\theta) P(Z_i|\theta) = \prod_{i=1}^N \left( \prod_{k=1}^K \phi(x_i|\mu_k,\sigma_k)^{Z_{ik}} w_k^{Z_{ik}} \right) }[/math]. Note how easy it is to write it thanks to the fact that we chose to use [math]\displaystyle{ Z_{ik} \in \{0,1\} }[/math] compare to [math]\displaystyle{ Z_i=k }[/math]. And here is the augmented-data log-likelihood (useful in the M step of the EM algorithm, see below): [math]\displaystyle{ l_{aug}(\theta) = \sum_{i=1}^N \left( \sum_{k=1}^K Z_{ik} ln(\phi(x_i|\mu_k,\sigma_k)) + \sum_{k=1}^K Z_{ik} ln(w_k) \right) }[/math] Using the plate notation, the Gaussian mixture model described here can be represented like this. EM algorithm - definition: we first define an objective function, [math]\displaystyle{ Q }[/math], which happens to be the conditional expectation of the augmented-data log-likelihood function, [math]\displaystyle{ l_{aug} }[/math], over the latent variables, [math]\displaystyle{ Z }[/math], given the observed data, [math]\displaystyle{ X }[/math], and the parameter estimates, [math]\displaystyle{ \theta }[/math]. The idea is to iterate two steps, starting from randomly-initialized parameters. In the E-step, one does an expectation, that is one computes this objective function to determine the membership probabilities. And in the M-step, one maximizes this objective function to determine the next iterate of the parameter estimates. In equations, it can be written like this: E step: [math]\displaystyle{ Q(\theta|X,\theta^{(t)}) = \mathbb{E}_{Z|X,\theta^{(t)}} \left[ ln(P(X,Z|\theta))|X,\theta^{(t)} \right] = \int q(Z|X,\theta^{(t)}) \; l_{aug} \; dZ }[/math] M-step: [math]\displaystyle{ \theta^{(t+1)} = argmax_{\theta} Q(\theta|X,\theta^{(t)}) }[/math] so that [math]\displaystyle{ \forall \theta \in \Theta, Q(\theta^{(t+1)}|X,\theta^{(t)}) \ge Q(\theta|X,\theta^{(t)}) }[/math] EM algorithm - theory: stated like this above doesn't necessarily allow oneself to understand it immediately, at least in my case. Hopefully, Matthew Beal presents it in a great and simple way in his PhD thesis (see references at the bottom of the page). Here is the observed-data log-likelihood: [math]\displaystyle{ l_{obs}(\theta) = \sum_{i=1}^N ln \left( f(x_i|\theta) \right) }[/math] First we introduce the hidden variables by integrating them out: [math]\displaystyle{ l_{obs}(\theta) = \sum_{i=1}^N ln \left( \int p(x_i,z_i|\theta) dz_i \right) }[/math] Then, we use any probability distribution [math]\displaystyle{ q }[/math] on these hidden variables (in fact, we use a distinct distribution [math]\displaystyle{ q_{z_i} }[/math] for each observation): [math]\displaystyle{ l_{obs}(\theta) = \sum_{i=1}^N ln \left( \int q_{z_i}(z_i) \frac{p(x_i,z_i|\theta)}{q_{z_i}(z_i)} dz_i \right) }[/math] And here is the great trick, as explained by Beal: "any probability distribution over the hidden variables gives rise to a lower bound on [math]\displaystyle{ l_{obs} }[/math]". This is due to to the Jensen inequality (the logarithm is concave): [math]\displaystyle{ l_{obs}(\theta) \ge \sum_{i=1}^N \int q_{z_i}(z_i) ln \left( \frac{p(x_i,z_i|\theta)}{q_{z_i}(z_i)} \right) dz_i = \mathcal{F}(q_{z_1}(z_1), ..., q_{z_N}(z_N), \theta) }[/math] At each iteration, the E step maximizes the lower bound ([math]\displaystyle{ \mathcal{F} }[/math]) with respect to the [math]\displaystyle{ q_{z_i}(z_i) }[/math]: E step: [math]\displaystyle{ q^{(t+1)}_{z_i} \leftarrow argmax_{q_{z_i}} \mathcal{F}(q_z(z), \theta^{(t)}) \forall i }[/math] M step: [math]\displaystyle{ \theta^{(t+1)} \leftarrow argmax_\theta \mathcal{F}(q^{(t+1)}_z(z), \theta) }[/math] The E-step amounts to inferring the posterior distribution of the hidden variables [math]\displaystyle{ q^{(t+1)}_{z_i} }[/math] given the current parameter [math]\displaystyle{ \theta^{(t)} }[/math]: [math]\displaystyle{ q^{(t+1)}_{z_i}(z_i) = p(z_i | x_i, \theta^{(t)}) }[/math] Indeed, the [math]\displaystyle{ q^{(t+1)}_{z_i}(z_i) }[/math] make the bound tight (the inequality becomes an equality): [math]\displaystyle{ \mathcal{F}(q^{(t+1)}_z(z), \theta^{(t)}) = \sum_{i=1}^N \int q^{(t+1)}_{z_i}(z_i) ln \left( \frac{p(x_i,z_i|\theta^{(t)})}{q^{(t+1)}_{z_i}(z_i)} \right) dz_i }[/math] [math]\displaystyle{ \mathcal{F}(q^{(t+1)}_z(z), \theta^{(t)}) = \sum_{i=1}^N \int p(z_i | x_i, \theta^{(t)}) ln \left( \frac{p(x_i,z_i|\theta^{(t)})}{p(z_i | x_i, \theta^{(t)})} \right) dz_i }[/math] [math]\displaystyle{ \mathcal{F}(q^{(t+1)}_z(z), \theta^{(t)}) = \sum_{i=1}^N \int p(z_i | x_i, \theta^{(t)}) ln \left( \frac{p(x_i|\theta^{(t)}) p(z_i|x_i,\theta^{(t)})}{p(z_i | x_i, \theta^{(t)})} \right) dz_i }[/math] [math]\displaystyle{ \mathcal{F}(q^{(t+1)}_z(z), \theta^{(t)}) = \sum_{i=1}^N \int p(z_i | x_i, \theta^{(t)}) ln \left( p(x_i|\theta^{(t)}) \right) dz_i }[/math] [math]\displaystyle{ \mathcal{F}(q^{(t+1)}_z(z), \theta^{(t)}) = \sum_{i=1}^N ln \left( p(x_i|\theta^{(t)}) \right) }[/math] [math]\displaystyle{ \mathcal{F}(q^{(t+1)}_z(z), \theta^{(t)}) = l_{obs}(\theta^{(t)}) }[/math] Then, at the M step, we use these statistics to maximize the new lower bound [math]\displaystyle{ \mathcal{F} }[/math] with respect to [math]\displaystyle{ \theta }[/math], and therefore find [math]\displaystyle{ \theta^{(t+1)} }[/math]. EM algorithm - variational: if the posterior distributions [math]\displaystyle{ p(z_i|x_i,\theta) }[/math] are intractable, we can use a variational approach to constrain them to be of a particular, tractable form. In the E step, maximizing [math]\displaystyle{ \mathcal{F} }[/math] with respect to [math]\displaystyle{ q_{z_i} }[/math] is equivalent to minimizing the Kullback-Leibler divergence between the variational distribution [math]\displaystyle{ q(z_i) }[/math] and the exact hidden variable posterior [math]\displaystyle{ p(z_i|x_i,\theta) }[/math]: [math]\displaystyle{ KL[q_{z_i}(z_i) || p(z_i|x_i,\theta)] = \int q_{z_i}(z_i) ln \left( \frac{q_{z_i}(z_i)}{p(z_i|x_i,\theta)} \right) }[/math] As a result, the E step may not always lead to a tight bound. Formulas of both steps: in both steps we need to use [math]\displaystyle{ Q }[/math], whether to evaluate it or maximize it. [math]\displaystyle{ Q(\theta|X,\theta^{(t)}) = \mathbb{E}_{Z|X,\theta^{(t)}} \left[ ln(P(X,Z|\theta))|X,\theta^{(t)} \right] }[/math] [math]\displaystyle{ Q(\theta|X,\theta^{(t)}) = \mathbb{E}_{Z|X,\theta^{(t)}} \left[ l_{aug}(\theta)|X,\theta^{(t)} \right] }[/math] [math]\displaystyle{ Q(\theta|X,\theta^{(t)}) = \sum_{i=1}^N \left( \sum_{k=1}^K \mathbb{E}_{Z|X,\theta^{(t)}}[Z_{ik}|x_i,\theta_k^{(t)}] \; ln(\phi(x_i|\mu_k,\sigma_k)) + \sum_{k=1}^K \mathbb{E}_{Z|X,\theta^{(t)}}[Z_{ik}|x_i,\theta_k^{(t)}] \; ln(w_k) \right) }[/math] Formulas of the E step: as indicated above, the E step consists in evaluating [math]\displaystyle{ Q }[/math], i.e. simply evaluating the conditional expectation over the latent variables of the augmented-data log-likelihood given the observed data and the current estimates of the parameters. [math]\displaystyle{ \mathbb{E}_{Z|X,\theta^{(t)}}[Z_{ik}|x_i,\theta_k^{(t)}] = 1 \times P(Z_{ik}=1|x_i,\theta_k^{(t)}) + 0 \times P(Z_{ik}=0|x_i,\theta_k^{(t)}) = P(Z_{ik}=1|x_i,\theta_k^{(t)}) = \frac{w_k^{(t)} \phi(x_i|\mu_k^{(t)},\sigma_k^{(t)})}{\sum_{l=1}^K w_l^{(t)} \phi(x_i|\mu_l^{(t)},\sigma_l^{(t)})} = p(k|i) }[/math] Note how the conditional expectation over [math]\displaystyle{ Z_{ik} }[/math] simply happens to be the posterior of [math]\displaystyle{ Z_{ik}=1 }[/math], which of course corresponds to the membership probability. Formulas of the M step: in this step, we need to maximize [math]\displaystyle{ Q }[/math] (also written [math]\displaystyle{ \mathcal{F} }[/math] above), w.r.t. each [math]\displaystyle{ \theta_k }[/math]. A few important rules are required to write down the analytical formulas of the MLEs, but only from a high-school level (see here). M step - weights: let's start by finding the maximum-likelihood estimates of the weights [math]\displaystyle{ w_k }[/math]. But remember the constraint [math]\displaystyle{ \sum_{k=1}^K w_k = 1 }[/math]. To enforce it, we can use a Lagrange multiplier, [math]\displaystyle{ \lambda }[/math]. This means that we now need to maximize the following equation where [math]\displaystyle{ \Lambda }[/math] is a Lagrange function (only the part of Q being a function of the weights is kept): [math]\displaystyle{ \Lambda(w_k,\lambda) = \sum_{i=1}^N \left( \sum_{k=1}^K p(k|i) ln(w_k) \right) + \lambda (1 - \sum_{k=1}^K w_k) }[/math] As usual, to find the maximum, we derive and equal to zero: [math]\displaystyle{ \frac{\partial \Lambda}{\partial w_k}(w_k) = \sum_{i=1}^N \left( p(k|i) \frac{1}{w_k} \right) - \lambda }[/math] [math]\displaystyle{ \frac{\partial \Lambda}{\partial w_k}(\hat{w}_k^{(t+1)}) = 0 }[/math] [math]\displaystyle{ \hat{w}_k^{(t+1)} = \frac{1}{\lambda} \sum_{i=1}^N p(k|i) }[/math] Now, to find the multiplier, we go back to the constraint: [math]\displaystyle{ \sum_{k=1}^K \hat{w}_k^{(t+1)} = 1 \rightarrow \lambda = \sum_{i=1}^N \sum_{k=1}^K p(k|i) = N }[/math] Finally: [math]\displaystyle{ \hat{w}_k^{(t+1)} = \frac{1}{N} \sum_{i=1}^N p(k|i) }[/math] M step - means: [math]\displaystyle{ \frac{\partial Q}{\partial \mu_k} = \sum_{i=1}^N p(k|i) \frac{\partial ln(\phi(x_i|\mu_k,\sigma_k))}{\partial \mu_k} }[/math] [math]\displaystyle{ \frac{\partial Q}{\partial \mu_k} = \sum_{i=1}^N p(k|i) \frac{1}{\phi(x_i|\mu_k,\sigma_k)} \frac{\partial \phi(x_i|\mu_k,\sigma_k)}{\partial \mu_k} }[/math] [math]\displaystyle{ \frac{\partial Q}{\partial \mu_k} = 0 = \sum_{i=1}^N p(k|i) (x_i - \hat{\mu}_k^{(t+1)}) }[/math] Finally: [math]\displaystyle{ \hat{\mu}_k^{(t+1)} = \frac{\sum_{i=1}^N p(k/i) x_i}{\sum_{i=1}^N p(k/i)} }[/math] M step - variances: same kind of algebra [math]\displaystyle{ \frac{\partial Q}{\partial \sigma_k} = \sum_{i=1}^N p(k/i) (\frac{-1}{\sigma_k} + \frac{(x_i - \mu_k)^2}{\sigma_k^3}) }[/math] [math]\displaystyle{ \hat{\sigma}_k^{(t+1)} = \sqrt{\frac{\sum_{i=1}^N p(k/i) (x_i - \hat{\mu}_k^{(t+1)})^2}{\sum_{i=1}^N p(k/i)}} }[/math] M step - weights (2): we can write them in terms of unconstrained variables [math]\displaystyle{ \gamma_k }[/math] (softmax function): [math]\displaystyle{ w_k = \frac{e^{\gamma_k}}{\sum_{k=1}^K e^{\gamma_k}} }[/math] [math]\displaystyle{ \frac{\partial w_k}{\partial \gamma_j} = \begin{cases} w_k - w_k^2 & \mbox{if }j = k \\ -w_kw_j & \mbox{otherwise} \end{cases} }[/math] [math]\displaystyle{ \frac{\partial l(\theta)}{\partial w_k} = \sum_{i=1}^N (p(k/i) - w_k) }[/math] Finally: [math]\displaystyle{ \hat{w}_k = \frac{1}{N} \sum_{i=1}^N p(k/i) }[/math] R code to simulate data: if you read up to there, nothing is better than implementing the EM algorithm yourself! #' Generate univariate observations from a mixture of Normals #' #' @param K number of components #' @param N number of observations #' @param gap difference between all component means GetUnivariateSimulatedData <- function(K=2, N=100, gap=6){ mus <- seq(0, gap*(K-1), gap) sigmas <- runif(n=K, min=0.5, max=1.5) tmp <- floor(rnorm(n=K-1, mean=floor(N/K), sd=5)) ns <- c(tmp, N - sum(tmp)) clusters <- as.factor(matrix(unlist(lapply(1:K, function(k){rep(k, ns[k])})), ncol=1)) obs <- matrix(unlist(lapply(1:K, function(k){ rnorm(n=ns[k], mean=mus[k], sd=sigmas[k]) }))) new.order <- sample(1:N, N) obs <- obs[new.order] rownames(obs) <- NULL clusters <- clusters[new.order] return(list(obs=obs, clusters=clusters, mus=mus, sigmas=sigmas, mix.weights=ns/N)) } R code for the E step: #' Return probas of latent variables given data and parameters from previous iteration #' #' @param data Nx1 vector of observations #' @param params list which components are mus, sigmas and mix.weights Estep <- function(data, params){ GetMembershipProbas(data, params$mus, params$sigmas, params$mix.weights) } #' Return the membership probabilities P(zi=k/xi,theta) #' #' @param data Nx1 vector of observations #' @param mus Kx1 vector of means #' @param sigmas Kx1 vector of std deviations #' @param mix.weights Kx1 vector of mixture weights w_k=P(zi=k/theta) #' @return NxK matrix of membership probas GetMembershipProbas <- function(data, mus, sigmas, mix.weights){ N <- length(data) K <- length(mus) tmp <- matrix(unlist(lapply(1:N, function(i){ x <- data[i] norm.const <- sum(unlist(Map(function(mu, sigma, mix.weight){ mix.weight * GetUnivariateNormalDensity(x, mu, sigma)}, mus, sigmas, mix.weights))) unlist(Map(function(mu, sigma, mix.weight){ mix.weight * GetUnivariateNormalDensity(x, mu, sigma) / norm.const }, mus[-K], sigmas[-K], mix.weights[-K])) })), ncol=K-1, byrow=TRUE) membership.probas <- cbind(tmp, apply(tmp, 1, function(x){1 - sum(x)})) names(membership.probas) <- NULL return(membership.probas) } #' Univariate Normal density GetUnivariateNormalDensity <- function(x, mu, sigma){ return( 1/(sigma * sqrt(2*pi)) * exp(-1/(2*sigma^2)*(x-mu)^2) ) } R code for the M step: #' Return ML estimates of parameters #' #' @param data Nx1 vector of observations #' @param params list which components are mus, sigmas and mix.weights #' @param membership.probas NxK matrix with entry i,k being P(zi=k/xi,theta) Mstep <- function(data, params, membership.probas){ params.new <- list() sum.membership.probas <- apply(membership.probas, 2, sum) params.new$mus <- GetMlEstimMeans(data, membership.probas, sum.membership.probas) params.new$sigmas <- GetMlEstimStdDevs(data, params.new$mus, membership.probas, sum.membership.probas) params.new$mix.weights <- GetMlEstimMixWeights(data, membership.probas, sum.membership.probas) return(params.new) } #' Return ML estimates of the means (1 per cluster) #' #' @param data Nx1 vector of observations #' @param membership.probas NxK matrix with entry i,k being P(zi=k/xi,theta) #' @param sum.membership.probas Kx1 vector of sum per column of matrix above #' @return Kx1 vector of means GetMlEstimMeans <- function(data, membership.probas, sum.membership.probas){ K <- ncol(membership.probas) sapply(1:K, function(k){ sum(unlist(Map("*", membership.probas[,k], data))) / sum.membership.probas[k] }) } #' Return ML estimates of the std deviations (1 per cluster) #' #' @param data Nx1 vector of observations #' @param membership.probas NxK matrix with entry i,k being P(zi=k/xi,theta) #' @param sum.membership.probas Kx1 vector of sum per column of matrix above #' @return Kx1 vector of std deviations GetMlEstimStdDevs <- function(data, means, membership.probas, sum.membership.probas){ K <- ncol(membership.probas) sapply(1:K, function(k){ sqrt(sum(unlist(Map(function(p.ki, x.i){ p.ki * (x.i - means[k])^2 }, membership.probas[,k], data))) / sum.membership.probas[k]) }) } #' Return ML estimates of the mixture weights #' #' @param data Nx1 vector of observations #' @param membership.probas NxK matrix with entry i,k being P(zi=k/xi,theta) #' @param sum.membership.probas Kx1 vector of sum per column of matrix above #' @return Kx1 vector of mixture weights GetMlEstimMixWeights <- function(data, membership.probas, sum.membership.probas){ K <- ncol(membership.probas) sapply(1:K, function(k){ 1/length(data) * sum.membership.probas[k] }) } R code for the log-likelihood: GetLogLikelihood <- function(data, mus, sigmas, mix.weights){ loglik <- sum(sapply(data, function(x){ log(sum(unlist(Map(function(mu, sigma, mix.weight){ mix.weight * GetUnivariateNormalDensity(x, mu, sigma) }, mus, sigmas, mix.weights)))) })) return(loglik) } R code for the EM loop: EMalgo <- function(data, params, threshold.convergence=10^(-2), nb.iter=10, verbose=1){ logliks <- vector() i <- 1 if(verbose > 0) cat(paste("iter ", i, "\n", sep="")) membership.probas <- Estep(data, params) params <- Mstep(data, params, membership.probas) loglik <- GetLogLikelihood(data, params$mus, params$sigmas, params$mix.weights) logliks <- append(logliks, loglik) while(i < nb.iter){ i <- i + 1 if(verbose > 0) cat(paste("iter ", i, "\n", sep="")) membership.probas <- Estep(data, params) params <- Mstep(data, params, membership.probas) loglik <- GetLogLikelihood(data, params$mus, params$sigmas, params$mix.weights) if(loglik < logliks[length(logliks)]){ msg <- paste("the log-likelihood is decreasing:", loglik, "<", logliks[length(logliks)]) stop(msg, call.=FALSE) } logliks <- append(logliks, loglik) if(abs(logliks[i] - logliks[i-1]) <= threshold.convergence) break } return(list(params=params, membership.probas=membership.probas, logliks=logliks, nb.iters=i)) } Example: and now, let's try it! ## simulate data K <- 3 N <- 300 simul <- GetUnivariateSimulatedData(K, N) data <- simul$obs ## run the EM algorithm params0 <- list(mus=runif(n=K, min=min(data), max=max(data)), sigmas=rep(1, K), mix.weights=rep(1/K, K)) res <- EMalgo(data, params0, 10^(-3), 1000, 1) ## check its convergence plot(res$logliks, xlab="iterations", ylab="log-likelihood", main="Convergence of the EM algorithm", type="b") ## plot the data along with the inferred densities png("mixture_univar_em.png") hist(data, breaks=30, freq=FALSE, col="grey", border="white", ylim=c(0,0.15), main="Histogram of data overlaid with densities inferred by EM") rx <- seq(from=min(data), to=max(data), by=0.1) ds <- lapply(1:K, function(k){dnorm(x=rx, mean=res$params$mus[k], sd=res$params$sigmas[k])}) f <- sapply(1:length(rx), function(i){ res$params$mix.weights[1] * ds[[1]][i] + res$params$mix.weights[2] * ds[[2]][i] + res$params$mix.weights[3] * ds[[3]][i] }) lines(rx, f, col="red", lwd=2) dev.off() It seems to work well, which was expected as the clusters are well separated from each other... The classification of each observation can be obtained via the following command: ## get the classification of the observations memberships <- apply(res$membership.probas, 1, function(x){which(x > 0.5)}) table(memberships) References: chapter 1 from the PhD thesis of Matthew Stephens (Oxford, 2000) freely available online chapter 2 from the PhD thesis of Matthew Beal (UCL, 2003) freely available online lecture "Mixture Models, Latent Variables and the EM Algorithm" from Cosma Shalizi freely available online talk "Graphical Models" from Zubin Ghahramani freely available online book "Introducing Monte Carlo Methods with R" from Robert and and Casella (2009) Beyond: many different distributions can be used besides the Normal the observations can be multivariate we can fit the model using Bayesian methods, e.g. MCMC or Variational Bayes we can try to estimate the number of components (K), e.g. by reversible-jump MCMC or via non-parametric Bayes there are issues, such as the fact that the EM can get stuck in a local maximum, or that the likelihood is invariant under permutations of the components' labels the parameters of each mixture component can depend on some known predictors, giving rise to mixture-of-experts models