Conditional independence statements
Conditional independence (CI) statements over a ground set $N$ are triples of pairwise disjoint subsets $I, J, K \subseteq N$ denoted as $[I \mathrel{⫫} J \mid K]$. The ground set indexes objects under consideration and the CI statement asserts that once the objects in $K$ are "controlled" (conditioned on, in statistical language), the objects in $I$ reveal no information about (are independent of) the objects in $J$.
The functionality documented here deals with CI statements are combinatorial objects. Collections of CI statements are often used to state Markov properties of graphical models in statistics and are ultimately used to define ideals. Their interpretations as polynomial equations depend on the ambient ring (markov_ring
or gaussian_ring
).
ci_stmt
— Methodci_stmt(I::Vector{<:VarName}, J::Vector{<:VarName}, K::Vector{<:VarName}; symmetric=true, semigraphoid=true)
A conditional independence statement asserting that I
is independent of J
given K
. These parameters are lists of names of random variables. The sets I
and J
must be disjoint as this package cannot yet deal with functional dependencies.
If symmetric
is true
, CI statements are assumed to be symmetric in their I
and J
components. The constructor then reorders the arguments to make the I
field lexicographically smaller than the J
to ensure that comparisons and hashing respect the symmetry.
If semigraphoid
is set to true
, the constructor also removes elements in the intersection of I
and K
from I
(and symetrically removes the intersection of J
and K
from J
).
As all three fields are sets, each of them may be deduplicated and sorted to ensure consistent comparison and hashing.
Examples
julia> ci_stmt(["A"], ["B"], ["X"])
[A _||_ B | X]
julia> ci_stmt(["1"], ["2", "3"], ["4", "5"])
[1 _||_ {2, 3} | {4, 5}]
This function is part of the experimental code in Oscar. Please read here for more details.
@CI_str
— MacroCI"I...,J...|K..."
A literal syntax for denoting CI statements is provided for cases in which all variable names consist of a single character. If I
and J
only consist of a single element, then even the comma may be omitted. Once the three sets are extracted, ci_stmt
is called.
Examples
julia> CI"AB|X"
[A _||_ B | X]
julia> CI"1,23|5424"
[1 _||_ 3 | {2, 4, 5}]
This function is part of the experimental code in Oscar. Please read here for more details.
==
— MethodBase.:(==)(lhs::CIStmt, rhs::CIStmt)
Compares CIStmt
s for identity in all their three fields.
This function is part of the experimental code in Oscar. Please read here for more details.
hash
— MethodBase.hash(stmt:;CIStmt, h::UInt)
Computes the hash of a CIStmt
.
This function is part of the experimental code in Oscar. Please read here for more details.
ci_statements
— Methodci_statements(random_variables::Vector{<:VarName})
Return a list of all elementary CI statements over a given set of variable names. A CIStmt(I, J, K)
is elementary if both I
and J
have only one element.
As a consequence of the semigraphoid properties, these statements are enough to describe the entire CI structure of a probability distribution.
Examples
julia> ci_statements(["A", "B", "X", "Y"])
24-element Vector{CIStmt}:
[A _||_ Y | {}]
[A _||_ Y | B]
[A _||_ Y | X]
[A _||_ Y | {B, X}]
[B _||_ Y | {}]
[B _||_ Y | A]
[B _||_ Y | X]
[B _||_ Y | {A, X}]
[X _||_ Y | {}]
[X _||_ Y | A]
⋮
[A _||_ X | {B, Y}]
[B _||_ X | {}]
[B _||_ X | A]
[B _||_ X | Y]
[B _||_ X | {A, Y}]
[A _||_ B | {}]
[A _||_ B | X]
[A _||_ B | Y]
[A _||_ B | {X, Y}]
This function is part of the experimental code in Oscar. Please read here for more details.
make_elementary
— Methodmake_elementary(stmt::CIStmt; semigaussoid=false)
Convert a CIStmt
into an equivalent list of CIStmt
s all of which are elementary. The default operation assumes the semigraphoid axioms and converts $[I \mathrel{⫫} J \mid K]$ into the list consisting of $[i \mathrel{⫫} j \mid L]$ for all $i \in I$, $j \in J$ and $L$ in the interval $K \subseteq L \subseteq (I \cup J \cup K) \setminus \{i,j\}$.
If semigaussoid
is true, the stronger semigaussoid axioms are assumed and L
in the above procedure does not range in the interval above K
but is always fixed to K
. Semigaussoids are also known as compositional graphoids.
Examples
julia> make_elementary(CI"12,34|56")
16-element Vector{CIStmt}:
[1 _||_ 3 | {5, 6}]
[1 _||_ 3 | {5, 6, 2}]
[1 _||_ 3 | {5, 6, 4}]
[1 _||_ 3 | {5, 6, 2, 4}]
[1 _||_ 4 | {5, 6}]
[1 _||_ 4 | {5, 6, 2}]
[1 _||_ 4 | {5, 6, 3}]
[1 _||_ 4 | {5, 6, 2, 3}]
[2 _||_ 3 | {5, 6}]
[2 _||_ 3 | {5, 6, 1}]
[2 _||_ 3 | {5, 6, 4}]
[2 _||_ 3 | {5, 6, 1, 4}]
[2 _||_ 4 | {5, 6}]
[2 _||_ 4 | {5, 6, 1}]
[2 _||_ 4 | {5, 6, 3}]
[2 _||_ 4 | {5, 6, 1, 3}]
julia> make_elementary(CI"12,34|56"; semigaussoid=true)
4-element Vector{CIStmt}:
[1 _||_ 3 | {5, 6}]
[1 _||_ 4 | {5, 6}]
[2 _||_ 3 | {5, 6}]
[2 _||_ 4 | {5, 6}]
This function is part of the experimental code in Oscar. Please read here for more details.