12345qiupeng
/
gtsam
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Tab copy errors

release/4.3a0
p-zach 2025-04-14 19:02:52 -04:00
parent 06a13bed70
commit 241d6dc07a
3 changed files with 3 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
gtsam/inference/doc/BayesNet.ipynb
View File

@ -17,7 +17,7 @@
"source": [
"A `BayesNet` in GTSAM represents a directed graphical model, specifically the result of running sequential variable elimination (like Cholesky or QR factorization) on a `FactorGraph`.\n",
"\n",
"It is essentially a collection of `Conditional` objects, ordered according to the elimination order. Each conditional represents $P(\text{variable} | \text{parents})$, where the parents are variables that appear later in the elimination ordering.\n",
"It is essentially a collection of `Conditional` objects, ordered according to the elimination order. Each conditional represents $P(\\text{variable} | \\text{parents})$, where the parents are variables that appear later in the elimination ordering.\n",
"\n",
"A Bayes net represents the joint probability distribution as a product of conditional probabilities stored in the net:\n",
"\n",

2
gtsam/inference/doc/BayesTree.ipynb
View File

@ -15,7 +15,7 @@
"id": "bayestree_desc_md"
},
"source": [
"A `BayesTree` is a graphical model that represents the result of multifrontal variable elimination on a `FactorGraph`. It is a tree structure where each node is a 'clique' containing a set of conditional distributions $P(\text{Frontals} | \text{Separator})$.\n",
"A `BayesTree` is a graphical model that represents the result of multifrontal variable elimination on a `FactorGraph`. It is a tree structure where each node is a 'clique' containing a set of conditional distributions $P(\\text{Frontals} | \\text{Separator})$.\n",
"\n",
"Each clique k contains a conditional $P(F_k∣S_k)$, where $F_k$ are frontal variables and $S_k$ are separator variables. The joint probability distribution encoded by the Bayes tree is given by the product of all clique conditionals:\n",
"\n",

2
gtsam/inference/doc/JunctionTree.ipynb
View File

@ -19,7 +19,7 @@
"\n",
"Key differences from related structures:\n",
"* **vs. EliminationTree:** Junction tree nodes can represent the elimination of multiple variables simultaneously (a 'frontal' set), whereas elimination tree nodes typically represent single variable eliminations.\n",
"* **vs. BayesTree:** A JunctionTree node contains the original factors associated with the variables being eliminated in that clique. A BayesTree node contains the *result* of eliminating those factors (i.e., a conditional density $P(\text{Frontals} | \text{Separator})$).\n",
"* **vs. BayesTree:** A JunctionTree node contains the original factors associated with the variables being eliminated in that clique. A BayesTree node contains the *result* of eliminating those factors (i.e., a conditional density $P(\\text{Frontals} | \\text{Separator})$).\n",
"\n",
"Like `EliminationTree`, direct manipulation of `JunctionTree` objects in Python is uncommon. It's primarily an internal structure used by `eliminateMultifrontal` when producing a `BayesTree`."
]