Factor Graphs


  • Goal
    • Introduction to Forney-style factor graphs and message passing algorithms
  • Materials
    • Mandatory
      • These lecture notes
      • Loeliger, 2007, pp. 1295-1300 (until section IV)
    • Optional
      • Video lecture by Frederico Wadehn (ETH Zurich) (highly recommended)

Why Factor Graphs?

  • A probabilistic inference task gets its computational load mainly through the need for marginalization (i.e., computing integrals). E.g., for a generative model $p(x_1,x_2,x_3,x_4,x_5)$, the inference task $p(x_2|x_3)$ is given by
$$\begin{align*} p(x_2|x_3) = \frac{\idotsint p(x_1,x_2,x_3,x_4,x_5) \, \mathrm{d}x_1 \mathrm{d}x_4 \mathrm{d}x_5}{\idotsint p(x_1,x_2,x_3,x_4,x_5) \, \mathrm{d}x_1 \mathrm{d}x_2 \mathrm{d}x_4 \mathrm{d}x_5} \end{align*}$$
  • Since these computations suffer from the "curse of dimensionality", we often need to solve a simpler problem in order to get an answer.
  • Factor graphs provide a computationally efficient approach to solving inference problems if the generative distribution can be factorized.
  • Factorization helps. For instance, if $p(x_1,x_2,x_3,x_4,x_5) = p(x_1)p(x_2,x_3)p(x_4)p(x_5|x_4)$, then
$$\begin{align*} p(x_2|x_3) &= \frac{\idotsint p(x_1)p(x_2,x_3)p(x_4)p(x_5|x_4) \, \mathrm{d}x_1 \mathrm{d}x_4 \mathrm{d}x_5}{\idotsint p(x_1)p(x_2,x_3)p(x_4)p(x_5|x_4) \, \mathrm{d}x_1 \mathrm{d}x_2 \mathrm{d}x_4 \mathrm{d}x_5} = \frac{p(x_2,x_3)}{\int p(x_2,x_3) \mathrm{d}x_2} \end{align*}$$

which is computationally much cheaper than the general case above.

  • In this lesson, we discuss how computationally efficient inference in factorized probability distributions can be automated.

Factor Graph Construction Rules

  • Consider a function $$ f(x_1,x_2,x_3,x_4,x_5) = f_a(x_1,x_2,x_3) \cdot f_b(x_3,x_4,x_5) \cdot f_c(x_4) $$
  • The factorization of this function can be graphically represented by a Forney-style Factor Graph (FFG):

  • An FFG is an undirected graph subject to the following construction rules (Forney, 2001)

    1. A node for every factor;
    2. An edge (or half-edge) for every variable;
    3. Node $g$ is connected to edge $x$ iff variable $x$ appears in factor $g$.

Some FFG Terminology

  • $f$ is called the global function and $f_\bullet$ are the factors.

  • A configuration is an assigment of values to all variables.

  • The configuration space is the set of all configurations, i.e., the domain of $f$

  • A configuration $\omega=(x_1,x_2,x_3,x_4,x_5)$ is said to be valid iff $f(\omega) \neq 0$

Equality Nodes for Branching Points

  • Note that a variable can appear in maximally two factors in an FFG (since an edge has only two end points).
  • Consider the factorization (where $x_2$ appears in three factors)
$$ f(x_1,x_2,x_3,x_4) = f_a(x_1,x_2)\cdot f_b(x_2,x_3) \cdot f_c(x_2,x_4) $$
  • For the factor graph representation, we will instead consider the function $g$, defined as $$\begin{align*} g(x_1,x_2&,x_2^\prime,x_2^{\prime\prime},x_3,x_4) = f_a(x_1,x_2)\cdot f_b(x_2^\prime,x_3) \cdot f_c(x_2^{\prime\prime},x_4) \cdot f_=(x_2,x_2^\prime,x_2^{\prime\prime}) \end{align*}$$ where $$ f_=(x_2,x_2^\prime,x_2^{\prime\prime}) \triangleq \delta(x_2-x_2^\prime)\, \delta(x_2-x_2^{\prime\prime}) $$

Equality Nodes for Branching Points, cont'd

  • Note that through introduction of auxiliary variables $X_2^\prime$ and $X_2^{\prime\prime}$ each variable in $g$ appears in maximally two factors.
  • The constraint $f_=(x,x^\prime,x^{\prime\prime})$ enforces that $X=X^\prime=X^{\prime\prime}$ for every valid configuration.
  • Since $f$ is a marginal of $g$, i.e., $$ f(x_1,x_2,x_3,x_4) = \iint g(x_1,x_2,x_2^\prime,x_2^{\prime\prime},x_3,x_4)\, \mathrm{d}x_2^\prime \mathrm{d}x_2^{\prime\prime} $$ it follows that any inference problem on $f$ can be executed by a corresponding inference problem on $g$, e.g., $$\begin{align*} f(x_1 \mid x_2) &\triangleq \frac{\iint f(x_1,x_2,x_3,x_4) \,\mathrm{d}x_3 \mathrm{d}x_4 }{ \idotsint f(x_1,x_2,x_3,x_4) \,\mathrm{d}x_1 \mathrm{d}x_3 \mathrm{d}x_4} \\ &= \frac{\idotsint g(x_1,x_2,x_2^\prime,x_2^{\prime\prime},x_3,x_4) \,\mathrm{d}x_2^\prime \mathrm{d}x_2^{\prime\prime} \mathrm{d}x_3 \mathrm{d}x_4 }{ \idotsint g(x_1,x_2,x_2^\prime,x_2^{\prime\prime},x_3,x_4) \,\mathrm{d}x_1 \mathrm{d}x_2^\prime \mathrm{d}x_2^{\prime\prime} \mathrm{d}x_3 \mathrm{d}x_4} \\ &\triangleq g(x_1 \mid x_2) \end{align*}$$
  • $\Rightarrow$ Any factorization of a global function $f$ can be represented by a Forney-style Factor Graph.

Probabilistic Models as Factor Graphs

  • FFGs can be used to express conditional independence (factorization) in probabilistic models.
  • For example, the (previously shown) graph for $f_a(x_1,x_2,x_3) \cdot f_b(x_3,x_4,x_5) \cdot f_c(x_4)$ could represent the probabilistic model $$ p(x_1,x_2,x_3,x_4,x_5) = p(x_1,x_2|x_3) \cdot p(x_3,x_5|x_4) \cdot p(x_4) $$ where we identify $$\begin{align*} f_a(x_1,x_2,x_3) &= p(x_1,x_2|x_3) \\ f_b(x_3,x_4,x_5) &= p(x_3,x_5|x_4) \\ f_c(x_4) &= p(x_4) \end{align*}$$
  • This is the graph

Inference by Closing Boxes

  • Factorizations provide opportunities to cut on the amount of needed computations when doing inference. In what follows, we will use FFGs to process these opportunities in an automatic way (i.e., by message passing).
  • Assume we wish to compute the marginal $$ \bar{f}(x_3) = \sum_{x_1,x_2,x_4,x_5,x_6,x_7}f(x_1,x_2,\ldots,x_7) $$ where $f$ is factorized as given by the following FFG

  • Due to the factorization, we can decompose this sum by the distributive law as $$\begin{align*} \bar{f}(x_3) = & \underbrace{ \left( \sum_{x_1,x_2} f_a(x_1)\,f_b(x_2)\,f_c(x_1,x_2,x_3)\right) }_{\overrightarrow{\mu}_{X_3}(x_3)} \\ & \underbrace{ \cdot\left( \sum_{x_4,x_5} f_d(x_4)\,f_e(x_3,x_4,x_5) \cdot \underbrace{ \left( \sum_{x_6,x_7} f_f(x_5,x_6,x_7)\,f_g(x_7)\right) }_{\overleftarrow{\mu}_{X_5}(x_5)} \right) }_{\overleftarrow{\mu}_{X_3}(x_3)} \end{align*}$$ which is computationally (much) lighter than executing the full sum $\sum_{x_1,\ldots,x_7}f(x_1,x_2,\ldots,x_7)$

  • Messages may flow in both directions on any edge (here on $X_3$). We often draw directed edges in a FFG in order to distinguish forward messages $\overrightarrow{\mu}_\bullet(\cdot)$ (in the same direction as the arrow of the edge) from backward messages $\overleftarrow{\mu}_\bullet(\cdot)$ (in opposite direction). With directed edges, the FFG looks as follows:

  • Crucially, drawing arrows on edges is only meant as a notational convenience. Technically, an FFG is an undirected graph.
  • Note that $\overleftarrow{\mu}_{X_5}(x_5)$ is obtained by multiplying all enclosed factors ($f_f$, $f_g$) by the green dashed box, followed by marginalization over all enclosed variables ($x_6$, $x_7$).
  • This is the Closing the Box-rule, which is a general recipe for marginalization of hidden variables and leads to a new factor with outgoing (sum-product) message $$ \mu_{\text{SP}} = \sum_{ \stackrel{ \textrm{enclosed} }{ \textrm{variables} } } \;\prod_{\stackrel{ \textrm{enclosed} }{ \textrm{factors} }} $$

Evaluating the closing-the-box rule for individual nodes

  • First, closing a box around the terminal nodes leads to $\overrightarrow{\mu}_{X_1}(x_1) \triangleq f_a(x_1)$, $\overrightarrow{\mu}_{X_2}(x_2) \triangleq f_b(x_2)$ etc.
    • So, the message out of a terminal node is the factor itself.
    • (Exercise) Derive now that the message coming from the open end of a half-edge always equals $1$.
  • The messages from internal nodes evaluate to: $$\begin{align*} \overrightarrow{\mu}_{X_3}(x_3) &= \sum_{x_1,x_2} f_a(x_1) \,f_b(x_2) \,f_c(x_1,x_2,x_3) \\ &= \sum_{x_1,x_2} \overrightarrow{\mu}_{X_1}(x_1) \overrightarrow{\mu}_{X_2}(x_2) \,f_c(x_1,x_2,x_3) \\ \overleftarrow{\mu}_{X_5}(x_5) &= \sum_{x_6,x_7} f_f(x_5,x_6,x_7)\,f_g(x_7) \\ &= \sum_{x_6,x_7} \overrightarrow{\mu}_{X_7}(x_7)\, f_f(x_5,x_6,x_7) \\ \end{align*}$$
  • Crucially, all message update rules can be computed from information that is locally available at each node.

Sum-Product Algorithm

  • (Sum-Product update rule). This recursive pattern for computing messages applies generally and is called the Sum-Product update rule, which is really just a special case of the closing-the-box rule: For any node, the outgoing message is obtained by taking the product of all incoming messages and the node function, followed by summing out (marginalization) all incoming variables. What is left (the outgoing message) is a function of the outgoing variable only:
$$ \boxed{ \overrightarrow{\mu}_{Y}(y) = \sum_{x_1,\ldots,x_n} \overrightarrow{\mu}_{X_1}(x_1)\cdots \overrightarrow{\mu}_{X_n}(x_n) \,f(y,x_1,\ldots,x_n) } $$

  • (Sum-Product Theorem). If the factor graph for a function $f$ has no cycles, then the marginal $\bar{f}(x_3) = \sum_{x_1,x_2,x_4,x_5,x_6,x_7}f(x_1,x_2,\ldots,x_7)$ is given by the Sum-Product Theorem:
$$ \boxed{ \bar{f}(x_3) = \overrightarrow{\mu}_{X_3}(x_3)\cdot \overleftarrow{\mu}_{X_3}(x_3)} $$
  • (Sum-Product Algorithm). If folows that the marginal $\bar{f}(x_3) = \sum_{x_1,x_2,x_4,x_5,x_6,x_7}f(x_1,x_2,\ldots,x_7)$ can be efficiently computed through sum-product messages. Executing inference through SP message passing is called the Sum-Product Algorithm.
  • (Exercise) Verfiy for yourself that all maginals in a cycle-free graph (a tree) can be computed exactly by starting with messages at the terminals and working towards the root of the tree.

Processing Observations in a Factor Graph

  • Terminal nodes can be used describe observed variables, e.g., use a factor $$f_Y(y) = \delta(y-3)$$ to terminate the edge for variable $Y$ if $y=3$ is observed.
  • Consider a generative model $$p(x,y_1,y_2) = p(x)\,p(y_1|x)\,p(y_2|x) .$$
    • This model expresses the assumption that $Y_1$ and $Y_2$ are independent measurements of $X$.

  • Assume that we are interested in the posterior for $X$ after observing $Y_1= \hat y_1$ and $Y_2= \hat y_2$. The posterior for $X$ can be inferred by applying the sum-product algorithm to the following graph:

  • (Note that) we usually draw terminal nodes for observed variables in the graph by smaller solid-black squares. This is just to help the visualization of the graph, since the computational rules are no different than for other nodes.
  • (Exercise) Can you draw the messages that infer $p(x\,|\,y_1,y_2)$?


We'll use ForneyLab, a factor graph toolbox for Julia, to build the above graph, and perform sum-product message passing to infer the posterior $p(x|y_1,y_2)$. We assume $p(y_1|x)$ and $p(y_2|x)$ to be Gaussian likelihoods with known variances: $$\begin{align*} p(y_1\,|\,x) &= \mathcal{N}(y_1\,|\,x, v_{y1}) \\ p(y_2\,|\,x) &= \mathcal{N}(y_2\,|\,x, v_{y2}) \end{align*}$$ Under this model, the posterior is given by: $$\begin{align*} p(x\,|\,y_1,y_2) &\propto \overbrace{p(y_1\,|\,x)\,p(y_2\,|\,x)}^{\text{likelihood}}\,\overbrace{p(x)}^{\text{prior}} \\ &=\mathcal{N}(x\,|\,\hat{y}_1, v_{y1})\, \mathcal{N}(x\,|\,\hat{y}_2, v_{y2}) \, \mathcal{N}(x\,|\,m_x, v_x) \end{align*}$$ so we can validate the answer by solving the Gaussian multiplication manually.

In [5]:
using ForneyLab 

# Data
y1_hat = 1.0
y2_hat = 2.0

# Construct the factor graph
fg = FactorGraph()
@RV x  ~ GaussianMeanVariance(constant(0.0), constant(4.0), id=:x) # Node p(x)
@RV y1 ~ GaussianMeanVariance(x, constant(1.0))            # Node p(y1|x)
@RV y2 ~ GaussianMeanVariance(x, constant(2.0))            # Node p(y2|x)
Clamp(y1, y1_hat)                  # Terminal (clamp) node for y1
Clamp(y2, y2_hat)                  # Terminal (clamp) node for y2
# draw(fg) # draw the constructed factor graph

# Perform sum-product message passing
eval(Meta.parse(sumProductAlgorithm(x, name="_algo1"))) # Automatically derives a message passing schedule
x_marginal = step_algo1!(Dict())[:x] # Execute algorithm and collect marginal distribution of x
println("Sum-product message passing result: p(x|y1,y2) = 𝒩($(mean(x_marginal)),$(var(x_marginal)))")

# Calculate mean and variance of p(x|y1,y2) manually by multiplying 3 Gaussians (see lesson 4 for details)
v = 1 / (1/4 + 1/1 + 1/2)
m = v * (0/4 + y1_hat/1.0 + y2_hat/2.0)
println("Manual result: p(x|y1,y2) = 𝒩($(m), $(v))")
Sum-product message passing result: p(x|y1,y2) = 𝒩(1.1428571428571428,0.5714285714285714)
Manual result: p(x|y1,y2) = 𝒩(1.1428571428571428, 0.5714285714285714)

Example: SP Messages for the Equality Node

  • Let´s compute the SP messages for the equality node $f_=(x,y,z) = \delta(z-x)\delta(z-y)$:

$$\begin{align*} \overrightarrow{\mu}_{Z}(z) &= \iint \overrightarrow{\mu}_{X}(x) \overrightarrow{\mu}_{Y}(y) \,\delta(z-x)\delta(z-y) \,\mathrm{d}x \mathrm{d}y \\ &= \overrightarrow{\mu}_{X}(z) \int \overrightarrow{\mu}_{Y}(y) \,\delta(z-y) \,\mathrm{d}y \\ &= \overrightarrow{\mu}_{X}(z) \overrightarrow{\mu}_{Y}(z) \end{align*}$$
  • By symmetry, this also implies (for the same equality node) that
$$\begin{align*} \overleftarrow{\mu}_{X}(x) &= \overrightarrow{\mu}_{Y}(x) \overleftarrow{\mu}_{Z}(x) \quad \text{and} \\ \overleftarrow{\mu}_{Y}(y) &= \overrightarrow{\mu}_{X}(y) \overleftarrow{\mu}_{Z}(y)\,. \end{align*}$$
  • Let us now consider the case of Gaussian messages $\overrightarrow{\mu}_{X}(x) = \mathcal{N}(\overrightarrow{m}_X,\overrightarrow{V}_X)$, $\overrightarrow{\mu}_{Y}(y) = \mathcal{N}(\overrightarrow{m}_Y,\overrightarrow{V}_Y)$ and $\overrightarrow{\mu}_{Z}(z) = \mathcal{N}(\overrightarrow{m}_Z,\overrightarrow{V}_Z)$. Let´s also define the precision matrices $\overrightarrow{W}_X \triangleq \overrightarrow{V}_X^{-1}$ and similarly for $Y$ and $Z$. Then applying the SP update rule leads to multiplication of two Gaussian distributions, resulting in
$$\begin{align*} \overrightarrow{W}_Z &= \overrightarrow{W}_X + \overrightarrow{W}_Y \\ \overrightarrow{W}_Z \overrightarrow{m}_z &= \overrightarrow{W}_X \overrightarrow{m}_X + \overrightarrow{W}_Y \overrightarrow{m}_Y \end{align*}$$
  • It follows that message passing through an equality node is similar to applying Bayes rule, i.e., fusion of two information sources. Does this make sense?

(OPTIONAL SLIDE) Example: SP Messages for the Addition Nodes

  • Next, consider an addition node $f_+(x,y,z) = \delta(z-x-y)$:
$$\begin{align*} \overrightarrow{\mu}_{Z}(z) &= \iint \overrightarrow{\mu}_{X}(x) \overrightarrow{\mu}_{Y}(y) \,\delta(z-x-y) \,\mathrm{d}x \mathrm{d}y \\ &= \int \overrightarrow{\mu}_{X}(x) \overrightarrow{\mu}_{Y}(z-x) \,\mathrm{d}x \,, \end{align*}$$

i.e., $\overrightarrow{\mu}_{Z}$ is the convolution of the messages $\overrightarrow{\mu}_{X}$ and $\overrightarrow{\mu}_{Y}$.

  • Of course, for Gaussian messages, these update rules evaluate to
$$\begin{align*} \overrightarrow{m}_Z = \overrightarrow{m}_X + \overrightarrow{m}_Y \,,\,\text{and}\,\,\overrightarrow{V}_z = \overrightarrow{V}_X + \overrightarrow{V}_Y \,. \end{align*}$$
  • Exercise: For the same summation node, work out the SP update rule for the *backward* message $\overleftarrow{\mu}_{X}(x)$ as a function of $\overrightarrow{\mu}_{Y}(y)$ and $\overleftarrow{\mu}_{Z}(z)$? And further refine the answer for Gaussian messages.

(OPTIONAL SLIDE) Example: SP Messages for Multiplication Nodes

  • Next, let us consider a multiplication by a fixed (invertible matrix) gain $f_A(x,y) = \delta(y-Ax)$

$$\begin{align*} \overrightarrow{\mu}_{Y}(y) = \int \overrightarrow{\mu}_{X}(x) \,\delta(y-Ax) \,\mathrm{d}x = \overrightarrow{\mu}_{X}(A^{-1}y) \,. \end{align*}$$
  • For a Gaussian message input message $\overrightarrow{\mu}_{X}(x) = \mathcal{N}(\overrightarrow{m}_{X},\overrightarrow{V}_{X})$, the output message is also Gaussian with $$\begin{align*} \overrightarrow{m}_{Y} = A\overrightarrow{m}_{X} \,,\,\text{and}\,\, \overrightarrow{V}_{Y} = A\overrightarrow{V}_{X}A^T \end{align*}$$ since $$\begin{align*} \overrightarrow{\mu}_{Y}(y) &= \overrightarrow{\mu}_{X}(A^{-1}y) \\ &\propto \exp \left( -\frac{1}{2} \left( A^{-1}y - \overrightarrow{m}_{X}\right)^T \overrightarrow{V}_{X}^{-1} \left( A^{-1}y - \overrightarrow{m}_{X}\right)\right) \\ &= \exp \left( -\frac{1}{2} \left( y - A\overrightarrow{m}_{X}\right)^T A^{-T}\overrightarrow{V}_{X}^{-1} A \left( y - A\overrightarrow{m}_{X}\right)\right) \\ &\propto \mathcal{N}(A\overrightarrow{m}_{X},A\overrightarrow{V}_{X}A^T) \,. \end{align*}$$
  • Excercise: Proof that, for the same factor $\delta(y-Ax)$ and Gaussian messages, the (backward) sum-product message $\overleftarrow{\mu}_{X}$ is given by $$\begin{align*} \overleftarrow{\xi}_{X} &= A^T\overleftarrow{\xi}_{Y} \\ \overleftarrow{W}_{X} &= A^T\overleftarrow{W}_{Y}A \end{align*}$$ where $\overleftarrow{\xi}_X \triangleq \overleftarrow{W}_X \overleftarrow{m}_X$ and $\overleftarrow{W}_{X} \triangleq \overleftarrow{V}_{X}^{-1}$ (and similarly for $Y$).



Let's calculate the Gaussian forward and backward messages for the addition node in ForneyLab.

In [2]:
# Forward message towards Z
fg = FactorGraph()
@RV x ~ GaussianMeanVariance(constant(1.0), constant(1.0), id=:x) 
@RV y ~ GaussianMeanVariance(constant(2.0), constant(1.0), id=:y)
@RV z = x + y; z.id = :z

eval(Meta.parse(sumProductAlgorithm(z, name="_z_fwd")))
msg_forward_Z = step_z_fwd!(Dict())[:z]
print("Forward message on Z: $(msg_forward_Z)")

# Backward message towards X
fg = FactorGraph()
@RV x = Variable(id=:x)
@RV y ~ GaussianMeanVariance(constant(2.0), constant(1.0), id=:y)
@RV z = x + y
GaussianMeanVariance(z, constant(3.0), constant(1.0), id=:z) 

eval(Meta.parse(sumProductAlgorithm(x, name="_x_bwd")))
msg_backward_X = step_x_bwd!(Dict())[:x]
print("Backward message on X: $(msg_backward_X)")
Forward message on Z: 𝒩(m=3.00, v=2.00)
Backward message on X: 𝒩(m=1.00, v=2.00)



In the same way we can also investigate the forward and backward messages for the gain node

In [3]:
# Forward message towards Y
fg = FactorGraph()
@RV x ~ GaussianMeanVariance(constant(1.0), constant(1.0), id=:x)
@RV y = constant(4.0) * x; y.id = :y

eval(Meta.parse(sumProductAlgorithm(y, name="_y_fwd")))
msg_forward_Y = step_y_fwd!(Dict())[:y]
print("Forward message on Y: $(msg_forward_Y)")
Forward message on Y: 𝒩(m=4.00, v=16.00)
In [4]:
# Backward message towards X
fg = FactorGraph()
x = Variable(id=:x)
@RV y = constant(4.0) * x
GaussianMeanVariance(y, constant(2.0), constant(1.0))

eval(Meta.parse(sumProductAlgorithm(x, name="_x_fwd2")))
msg_backward_X = step_x_fwd2!(Dict())[:x]
print("Backward message on X: $(msg_backward_X)")
Backward message on X: 𝒩(xi=8.00, w=16.00)

Example: Bayesian Linear Regression

  • Recall: the goal of regression is to estimate an unknown function from a set of (noisy) function values.
  • Assume we want to estimate some function $f: \mathbb{R}^D \rightarrow \mathbb{R}$ from data set $D = \{(x_1,y_1), \ldots, (x_N,y_N)\}$, where $y_i = f(x_i) + \epsilon_i$.
  • We will assume a linear model with white Gaussian noise and a Gaussian prior on the coefficients $w$: $$\begin{align*} y_i &= w^T x_i + \epsilon_i \\ \epsilon_i &\sim \mathcal{N}(0, \sigma^2) \\ w &\sim \mathcal{N}(0,\Sigma) \end{align*}$$ or equivalently $$\begin{align*} p(D,w) &= \overbrace{p(w)}^{\text{weight prior}} \prod_{i=1}^N \overbrace{p(y_i\,|\,x_i,w,\epsilon_i)}^{\text{regression model}} \overbrace{p(\epsilon_i)}^{\text{noise model}} \\ &= \mathcal{N}(w\,|\,0,\Sigma) \prod_{i=1}^N \delta(y_i - w^T x_i - \epsilon_i) \mathcal{N}(\epsilon_i\,|\,0,\sigma^2) \end{align*}$$
  • We are interested in inferring the posterior $p(w|D)$
  • Here's the factor graph for this model


Let's build the factor graph in Julia (with the FFG toolbox ForneyLab)

In [5]:
using PyPlot, ForneyLab, LinearAlgebra

# Parameters
Σ = 1e5 * Diagonal(I,3) # Covariance matrix of prior on w
σ2 = 2.0         # Noise variance

# Generate data set
w = [1.0; 2.0; 0.25]
N = 30
z = 10.0*rand(N)
x_train = [[1.0; z; z^2] for z in z] # Feature vector x = [1.0; z; z^2]
f(x) = (w'*x)[1]
y_train = map(f, x_train) + sqrt(σ2)*randn(N) # y[i] = w' * x[i] + ϵ
scatter(z, y_train); xlabel(L"z"); ylabel(L"f([1.0, z, z^2]) + \epsilon")
PyObject Text(29.88125000000001, 0.5, '$f([1.0, z, z^2]) + \\epsilon$')


Perform sum-product message passing and plot result (mean of posterior)

In [6]:
# Build factorgraph

fg = FactorGraph()
@RV w ~ GaussianMeanVariance(constant(zeros(3)), constant(Σ, id=), id=:w) # p(w)
for t=1:N
    x_t = Variable(id=:x_*t)
    d_t = Variable(id=:d_*t) # d=w'*x
    DotProduct(d_t, x_t, w) # p(f|w,x)
    @RV y_t ~ GaussianMeanVariance(d_t, constant(σ2, id=:σ2_*t), id=:y_*t) # p(y|d)
    placeholder(x_t, :x, index=t, dims=(3,))
    placeholder(y_t, :y, index=t);

# Build and run message passing algorithm
data = Dict(:x => x_train, :y => y_train)
w_posterior_dist = step!(data)[:w]

# Plot result
println("Posterior distribution of w: $(w_posterior_dist)")
scatter(z, y_train); xlabel(L"z"); ylabel(L"f([1.0, z, z^2]) + \epsilon");
z_test = collect(0:0.2:12)
x_test = [[1.0; z; z^2] for z in z_test]
for sample=1:10
    w = ForneyLab.sample(w_posterior_dist)
    f_est(x) = (w'*x)[1]
    plot(z_test, map(f_est, x_test), "k-", alpha=0.3);
Posterior distribution of w: 𝒩(xi=[3.18e+02, 2.18e+03, 1.63e+04], w=[[15.00, 83.82, 5.57e+02][83.82, 5.57e+02, 4.06e+03][5.57e+02, 4.06e+03, 3.13e+04]])

Homework Exercises

  • (Ex.1) Reflect on the fact that we now have methods for both marginalization and processing observations in FFGs. In principle, we are sufficiently equipped to do inference in probabilistic models through message passing. Draw the graph for $$p(x_1,x_2,x_3)=f_a(x_1)\cdot f_b(x_1,x_2)\cdot f_c(x_2,x_3)$$ and show which boxes need to be closed for computing $p(x_1|x_2)$.
  • (Ex.2) Consider a variable $X$ with measurements $D=\{x_1,x_2\}$. We assume the following model for $X$: $$\begin{align*} p(D,\theta) &= p(\theta)\cdot \prod_{n=1}^2 p(x_n|\theta) \\ p(\theta) &= \mathcal{N}(\theta \mid 0,1) \\ p(x_n \mid\theta) &= \mathcal{N}(x_n \mid \theta,1) \end{align*}$$
    • Draw the factor graph and infer $\theta$ through the Sum-Product Algorithm.

Message Passing in State-space Models

Consider the Markov state-space model
$$ p(x^T,z^T) = \underbrace{p(z_1)}_{\text{initial state}} \prod_{t=2}^T \underbrace{p(z_t\,|\,z_{t-1})}_{\text{state transitions}}\,\prod_{t=1}^T \underbrace{p(x_t\,|\,z_t)}_{\text{observations}} $$

  • The Forney-style factor graph for a state-space model:

  • In principle, for linear Gaussian models these inference tasks can be analytically solved, see e.g. Faragher, 2012 and previous lesson on state-space models
    • These derivations quickly become quite laborious
  • Alternatively, we could specify the generative model in a (Forney-style) factor graph and use automated message passing to infer the posterior over the hidden variables. E.g., the message passing schedule for Kalman filtering looks like this:

The Cart Tracking Problem Revisited

We can solve the cart tracking problem of the Dynamical systems lesson by sum-product message passing in a factor graph like the one depicted above. All we have to do is create factor nodes for the state-transition model $p(z_t|z_{t-1})$ and the observation model $p(x_t|z_t)$. Then we just build the factor graph and let ForneyLab (factor graph toolbox) perform message passing.

We'll implement the following model:

$$\begin{align*} \begin{bmatrix} z_t \\ \dot{z_t}\end{bmatrix} &= \begin{bmatrix} 1 & \Delta t \\ 0 & 1\end{bmatrix} \begin{bmatrix} z_{t-1} \\ \dot z_{t-1}\end{bmatrix} + \begin{bmatrix} (\Delta t)^2/2 \\ \Delta t\end{bmatrix} u_t + \mathcal{N}(0,\Sigma_z) \\ \mathbf{x}_t &= \begin{bmatrix} z_t \\ \dot{z_t}\end{bmatrix} + \mathcal{N}(0,\Sigma_x) \end{align*}$$
In [2]:
using LinearAlgebra, ForneyLab
include("scripts/cart_tracking_helpers.jl") # implements required factor nodes + helper functions

# Specify the model parameters
Δt = 1.0                     # assume the time steps to be equal in size
A = [1.0 Δt;
     0.0 1.0]
b = [0.5*Δt^2; Δt] 
Σz = convert(Matrix,Diagonal([0.2*Δt; 0.1*Δt])) # process noise covariance
Σx = convert(Matrix,Diagonal([1.0; 2.0]))     # observation noise covariance;

# Generate noisy observations
n = 10                # perform 10 timesteps
z_start = [10.0; 2.0] # initial state
u = 0.2 * ones(n)     # constant input u
noisy_x = generateNoisyMeasurements(z_start, u, A, b, Σz, Σx);

Since the factor graph is just a concatination of $n$ identical "sections", we only have to specify a single section. When running the message passing algorithm we will explictly use the posterior of the previous timestep as prior in the next one. Let's build a section of the factor graph:

In [3]:
fg = FactorGraph()
z_prev_m = Variable(id=:z_prev_m); placeholder(z_prev_m, :z_prev_m, dims=(2,))
z_prev_v = Variable(id=:z_prev_v); placeholder(z_prev_v, :z_prev_v, dims=(2,2))
bu = Variable(id=:bu); placeholder(bu, :bu, dims=(2,))

@RV z_prev ~ GaussianMeanVariance(z_prev_m, z_prev_v, id=:z_prev) # p(z_prev)
@RV noise_z ~ GaussianMeanVariance(constant(zeros(2), id=:noise_z_m), constant(Σz, id=:noise_z_v)) # process noise
@RV z = constant(A, id=:A) * z_prev + bu + noise_z; z.id = :z # p(z|z_prev) (state transition model)
@RV x ~ GaussianMeanVariance(z, constant(Σx, id=:Σx)) # p(x|z) (observation model)
placeholder(x, :x, dims=(2,));
G 6947781036024724108 placeholder_z_prev_m 3201874967886993523 placeholder_x 12873030637990256788 𝒩 gaussianmeanvariance_2 3201874967886993523--12873030637990256788 x 1 out 1 out 10098682789622638638 noise_z_v 3342751978328243165 + addition_2 13718701879998900323 + addition_1 3342751978328243165--13718701879998900323 variable_2 1 out 2 in1 14382197035184157863 𝒩 gaussianmeanvariance_1 3342751978328243165--14382197035184157863 noise_z 1 out 3 in2 14500561023254425370 placeholder_z_prev_v 12873030637990256788--3342751978328243165 z 1 out 2 m 11639027009330125838 Σx 12873030637990256788--11639027009330125838 Σx 1 out 3 v 1541610477257584085 𝒩 z_prev 1541610477257584085--6947781036024724108 z_prev_m 1 out 2 m 1541610477257584085--14500561023254425370 z_prev_v 1 out 3 v 625576071683671775 placeholder_bu 13718701879998900323--625576071683671775 bu 1 out 3 in2 9547695080007273316 × multiplication_1 13718701879998900323--9547695080007273316 variable_1 1 out 2 in1 9400403980255537941 noise_z_m 12231684841601085031 A 14382197035184157863--10098682789622638638 noise_z_v 1 out 3 v 14382197035184157863--9400403980255537941 noise_z_m 1 out 2 m 9547695080007273316--1541610477257584085 z_prev 1 out 2 in1 9547695080007273316--12231684841601085031 A 1 out 3 a

Now that we've built the factor graph, we can perform Kalman filtering by inserting measurement data into the factor graph and performing message passing.

In [5]:
eval(Meta.parse(sumProductAlgorithm(z))) # build message passing algorithm
marginals = Dict()
messages = Array{Message}(undef,6)
z_prev_m_0 = zeros(2)
z_prev_v_0 = 1e8*Diagonal(I,2)
for t=1:n
    data = Dict(:x => noisy_x[t], :bu => b*u[t],:z_prev_m => z_prev_m_0, :z_prev_v => z_prev_v_0)
    step!(data, marginals, messages) # perform msg passing (single timestep)
    # Posterior of z becomes prior of z in the next timestep:
#     ForneyLab.ensureParameters!(marginals[:z], (:m, :v))
    z_prev_m_0 = ForneyLab.unsafeMean(marginals[:z])
    z_prev_v_0 = ForneyLab.unsafeCov(marginals[:z])
# Collect prediction p(z[n]|z[n-1]), measurement p(z[n]|x[n]), corrected prediction p(z[n]|z[n-1],x[n])
prediction      = messages[5].dist # the message index is found by manual inspection of the schedule
measurement     = messages[6].dist
corr_prediction = convert(ProbabilityDistribution{Multivariate, GaussianMeanVariance}, marginals[:z])

# Make a fancy plot of the prediction, noisy measurement, and corrected prediction after n timesteps
plotCartPrediction(prediction, measurement, corr_prediction);
𝒩(m=[40.95, 1.39], v=[[1.00, 0.00][0.00, 2.00]])

Recap: Inference in Linear Gaussian Models by Sum-Product Message Passing

  • The foregoing message update rules can be extended to all scenarios involving additions, fixed-gain multiplications and branching (equality nodes), thus creating a completely automatable inference framework for factorized linear Gaussian models.
  • The update rules for elementary and important node types can be put in a Table (see Tables 1 through 6 in Loeliger, 2007).
  • If the update rules for all node types in a graph have been tabulated, then inference by message passing comes down to a set of table-lookup operations. This also works for large graphs (where 'manual' inference becomes intractable).
  • If the graph contains no cycles, the Sum-Product Algorithm computes exact marginals for all hidden variables.
  • If the graph contains cycles, we have in principle an infinite tree without terminals. In this case, the SP Algorithm is not guaranteed to find exact marginals. In practice, if we apply the SP algorithm for just a few iterations we often find satisfying approximate marginals.
In [10]:
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