User API

@RV provides a convenient way to add Variables and FactorNodes to the graph.

Examples:

# Automatically create new Variable x, try to assign x.id = :x if this id is available
@RV x ~ GaussianMeanVariance(constant(0.0), constant(1.0))

# Explicitly specify the id of the Variable
@RV [id=:my_y] y ~ GaussianMeanVariance(constant(0.0), constant(1.0))

# Automatically assign z.id = :z if this id is not yet taken
@RV z = x + y

# Manual assignment
@RV [id=:my_u] u = x + y

# Just create a variable
@RV x
@RV [id=:my_x] x

A factor graph consisting of factor nodes and edges.

A Variable encompasses one or more edges in a FactorGraph.

Return currently active FactorGraph. Create one if there is none.

Description:

An addition constraint factor node.

f(out,in1,in2) = δ(in1 + in2 - out)

Interfaces:

1. out
2. in1
3. in2

Construction:

Addition(out, in1, in2, id=:some_id)

-(in1::Variable, in2::Variable)

A subtraction constraint based on the addition factor node.

Description:

Bernoulli factor node

out ∈ {0, 1}
p ∈ [0, 1]

f(out, p) = Ber(out|p) = p^out (1 - p)^{1 - out}

Interfaces:

1. out
2. p

Construction:

Bernoulli(id=:some_id)

Description:

Beta factor node

Real scalars
a > 0
b > 0

f(out, a, b) = Beta(out|a, b) = Γ(a + b)/(Γ(a) Γ(b)) out^{a - 1} (1 - out)^{b - 1}

Interfaces:

1. out
2. a
3. b

Construction:

Beta(id=:some_id)

Description:

Categorical factor node

The categorical node defines a one-dimensional probability
distribution over the normal basis vectors of dimension d

out ∈ {0, 1}^d where Σ_k out_k = 1
p ∈ [0, 1]^d, where Σ_k p_k = 1

f(out, p) = Cat(out | p)
          = Π_i p_i^{out_i}

Interfaces:

1. out
2. p

Construction:

Categorical(id=:some_id)

@ensureVariables(...) casts all non-Variable arguments to Variable through constant(arg).

Description:

A factor that clamps a variable to a constant value.

f(out) = δ(out - value)

Interfaces:

1. out

Construction:

Clamp(out, value, id=:some_id)
Clamp(value, id=:some_id)

constant creates a Variable which is linked to a new Clamp, and returns this variable.

y = constant(3.0, id=:y)

placeholder(...) creates a Clamp node and registers this node as a data placeholder with the current graph.

# Link variable y to buffer with id :y,
# indicate that Clamp will hold Float64 values.
placeholder(y, :y, datatype=Float64)

# Link variable y to index 3 of buffer with id :y.
# Specify the data type by passing a default value for the Clamp.
placeholder(y, :y, index=3, default=0.0)

# Indicate that the Clamp will hold an array of size `dims`,
# with Float64 elements.
placeholder(X, :X, datatype=Float64, dims=(3,2))

The @composite macro allows for defining custom (composite) nodes. Composite nodes allow for implementating of custom update rules that may be computationally more efficient or convenient. A composite node can be defined with or without an internal model. For detailed usage instructions we refer to the composite_nodes demo.

Description:

Contingency factor node

The contingency distribution is a multivariate generalization of
the categorical distribution. As a bivariate distribution, the
contingency distribution defines the joint probability
over two unit vectors. The parameter p encodes a contingency matrix
that specifies the probability of co-occurrence.

out1 ∈ {0, 1}^d1 where Σ_j out1_j = 1
out2 ∈ {0, 1}^d2 where Σ_k out2_k = 1
p ∈ [0, 1]^{d1 × d2}, where Σ_jk p_jk = 1

f(out1, out2, p) = Con(out1, out2 | p)
                 = Π_jk p_jk^{out1_j * out2_k}

A Contingency distribution over more than two variables requires
higher-order tensors as parameters; these are not implemented in ForneyLab.

Interfaces:

1. out1
2. out2
3. p

Construction:

Contingency(id=:some_id)

Description:

Dirichlet factor node

Multivariate:
f(out, a) = Dir(out|a)
          = Γ(Σ_i a_i)/(Π_i Γ(a_i)) Π_i out_i^{a_i}
where 'a' is a vector with every a_i > 0

Matrix variate:
f(out, a) = Π_k Dir(out|a_*k)
where 'a' represents a left-stochastic matrix with every a_jk > 0

Interfaces:

1. out
2. a

Construction:

Dirichlet(id=:some_id)

Description:

out = in1'*in2

in1: d-dimensional vector
in2: d-dimensional vector
out: scalar

       in2
       |
  in1  V   out
----->[⋅]----->

f(out, in1, in2) =  δ(out - in1'*in2)

Interfaces:

1 i[:out], 2 i[:in1], 3 i[:in2]

Construction:

DotProduct(out, in1, in2, id=:my_node)

Description:

An equality constraint factor node

f([1],[2],[3]) = δ([1] - [2]) δ([1] - [3])

Interfaces:

1, 2, 3

Construction:

Equality(id=:some_id)

The interfaces of an Equality node have to be connected manually.

Description:

Maps a location to a scale parameter by exponentiation

f(out,in1) = δ(out - exp(in1))

Interfaces:

1. out
2. in1

Construction:

Exponential(out, in1, id=:some_id)

Description:

A gamma node with shape-rate parameterization:

f(out,a,b) = Gam(out|a,b) = 1/Γ(a) b^a out^{a - 1} exp(-b out)

Interfaces:

1. out
2. a (shape)
3. b (rate)

Construction:

Gamma(out, a, b, id=:some_id)

Description:

A Gaussian with mean-precision parameterization:

f(out,m,w) = 𝒩(out|m,w) = (2π)^{-D/2} |w|^{1/2} exp(-1/2 (out - m)' w (out - m))

Interfaces:

1. out
2. m (mean)
3. w (precision)

Construction:

GaussianMeanPrecision(out, m, w, id=:some_id)

Description:

A Gaussian with mean-variance parameterization:

f(out,m,v) = 𝒩(out|m,v) = (2π)^{-D/2} |v|^{-1/2} exp(-1/2 (out - m)' v^{-1} (out - m))

Interfaces:

1. out
2. m (mean)
3. v (covariance)

Construction:

GaussianMeanVariance(out, m, v, id=:some_id)

Description:

A Gaussian mixture with mean-precision parameterization:

f(out, z, m1, w1, m2, w2, ...) = 𝒩(out|m1, w1)^z_1 * 𝒩(out|m2, w2)^z_2 * ...

Interfaces:

1. out
2. z (switch)
3. m1 (mean)
4. w1 (precision)
5. m2 (mean)
6. w2 (precision)
...

Construction:

GaussianMixture(out, z, m1, w1, m2, w2, ..., id=:some_id)

Description:

A Gaussian with weighted-mean-precision parameterization:

f(out,xi,w) = 𝒩(out|xi,w) = (2π)^{-D/2} |w|^{1/2} exp(-1/2 xi' w^{-1} xi) exp(-1/2 xi' w xi + out' xi)

Interfaces:

1. out
2. xi (weighted mean, w*m)
3. w (precision)

Construction:

GaussianWeightedMeanPrecision(out, xi, w, id=:some_id)

Description:

A log-normal node with location-scale parameterization:

f(out,m,s) = logN(out|m, s) = 1/out (2π s)^{-1/2} exp(-1/(2s) (log(out) - m)^2))

Interfaces:

1. out
2. m (location)
3. s (squared scale)

Construction:

LogNormal(out, m, s, id=:some_id)

Description:

Logit mapping between a real variable in1 ∈ R and a binary variable out ∈ {0, 1}.

f(out, in1, xi) =  Ber(out | σ(in1))
                >= exp(in1*out) σ(xi) exp[-(in1 + xi)/2 - λ(xi)(in1^2 - xi^2)], where
           σ(x) =  1/(1 + exp(-x))
           λ(x) =  (σ(x) - 1/2)/(2*x)

Interfaces:

1. out (binary)
2. in1 (real)
3. xi (auxiliary variable)

Construction:

Logit(out, in1, xi)

Description:

For continuous random variables, the multiplication node acts
as a (matrix) multiplication constraint, with node function

f(out, in1, a) = δ(out - a*in1)

Interfaces:

1. out
2. in1
3. a

Construction:

Multiplication(out, in1, a, id=:some_id)

Description:

Nonlinear node modeling a nonlinear relation. Updates for
the nonlinear node are computed through the unscented transform (by default), 
importance sampling, or local linear approximation.

For more details see "On Approximate Nonlinear Gaussian Message Passing on
Factor Graphs", Petersen et al. 2018.

f(out, in1) = δ(out - g(in1))

Interfaces:

1. out
2. in1

Construction:

Nonlinear{T}(out, in1; g=g, id=:my_node)
Nonlinear{T}(out, in1; g=g, g_inv=g_inv, id=:my_node)
Nonlinear{T}(out, in1, in2, ...; g=g, id=:my_node)
Nonlinear{T}(out, in1, in2, ...; g=g, g_inv=(g_inv_in1, g_inv_in2, ...), id=:my_node)
Nonlinear{T}(out, in1, in2, ...; g=g, g_inv=(g_inv_in1, nothing, ...), id=:my_node)

where T encodes the approximation method: Unscented, Sampling, or Extended.

Description:

Poisson factor node

Real scalars
l > 0 (rate)

f(out, l) = Poisson(out|l) = 1/(x!) * l^x * exp(-l)

Interfaces:

1. out
2. l

Construction:

Poisson(id=:some_id)

Description:

Constrains a continuous, real-valued variable in1 ∈ R with a binary (boolean) variable out ∈ {0, 1} through a probit link function.

f(out, in1) = Ber(out | Φ(in1))

Interfaces:

1. out (binary)
2. in1 (real)

Construction:

Probit(out, in1, id=:some_id)

Description:

Softmax mapping between a real variable in1 ∈ R^d and discrete variable out ∈ {0, 1}^d.

f(out, in1, xi, a) = Cat(out_j | exp(in1_j)/Σ_k exp(in1_k)),
where log(Σ_k exp(x_k)) is upper-bounded according to (Bouchard, 2007).

Interfaces:

1. out (discrete)
2. in1 (real)
3. xi  (auxiliary variable)
4. a   (auxiliary variable)

Construction:

Softmax(out, in1, xi, a)

Description:

The transition node models a transition between discrete
random variables, with node function

f(out, in1, a) = Cat(out | a*in1)

Where a is a left-stochastic matrix (columns sum to one)

Interfaces:

1. out
2. in1
3. a

Construction:

Transition(out, in1, a, id=:some_id)

Description:

A Wishart node:

f(out,v,nu) = W(out|v, nu) = B(v, nu) |out|^{(nu - D - 1)/2} exp(-1/2 tr(v^{-1} out))

Interfaces:

1. out
2. v (scale matrix)
3. nu (degrees of freedom)

Construction:

Wishart(out, v, nu, id=:some_id)

A MarginalTable defines the update order for marginal computations.

Initialize an empty PosteriorFactorization for sequential construction

Initialize a PosteriorFactorization consisting of a single PosteriorFactor for the entire graph

Construct a PosteriorFactorization consisting of one PosteriorFactor for each argument

An InferenceAlgorithm specifies the computations for the quantities of interest.

A Schedule defines the update order for message computations.

Return currently active InferenceAlgorithm. Create one if there is none.

Create a message passing algorithm to infer marginals over a posterior distribution

Generate Julia code for message passing and optional free energy evaluation

Encodes a message, which is a probability distribution with a scaling factor

PointMass is an abstract type used to describe point mass distributions. It never occurs in a FactorGraph, but it is used as a probability distribution type.

Encodes a probability distribution as a FactorFunction of type family with fixed interfaces

@symmetrical `function_definition`

Duplicate a method definition with the order of the first two arguments swapped. This macro is used to duplicate methods that are symmetrical in their first two input arguments, but require explicit definitions for the different argument orders. Example:

@symmetrical function prod!(x, y, z)
    ...
end

Matrix inversion using Cholesky decomposition, attempts with added regularization (1e-8*I) on failure.

Cast input to a Matrix if necessary

Helper function to call dynamically generated init functions

Check if arguments are approximately equal

Checks if input matrix is positive definite. We also perform rounding in order to prevent floating point precision problems that isposdef()` suffers from.

Wrapper for logabsbeta function that returns first element of its output

Wrapper for logabsgamma function that returns first element of its output

leaftypes(datatype) returns all subtypes of datatype that are leafs in the type tree.

Helper function to construct 1x1 Matrix

Helper function to call dynamically generated step! functions

Solve trigamma(y) = x for y.

Uses Newton's method on the convex function 1/trigramma(y). Iterations converge monotonically. Based on trigammaInverse implementation in R package "limma" by Gordon Smyth: https://github.com/Bioconductor-mirror/limma/blob/master/R/fitFDist.R