gtsam/doc/Hybrid.lyx

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\begin_body

\begin_layout Title
Hybrid Inference
\end_layout

\begin_layout Author
Frank Dellaert & Varun Agrawal
\end_layout

\begin_layout Date
January 2023
\end_layout

\begin_layout Section
Hybrid Conditionals
\end_layout

\begin_layout Standard
Here we develop a hybrid conditional density, on continuous variables (typically
 a measurement 
\begin_inset Formula $x$
\end_inset

), given a mix of continuous variables 
\begin_inset Formula $y$
\end_inset

 and discrete variables 
\begin_inset Formula $m$
\end_inset

.
 We start by reviewing a Gaussian conditional density and its invariants
 (relationship between density, error, and normalization constant), and
 then work out what needs to happen for a hybrid version.
\end_layout

\begin_layout Subsubsection*
GaussianConditional
\end_layout

\begin_layout Standard
A 
\emph on
GaussianConditional
\emph default
 is a properly normalized, multivariate Gaussian conditional density:
\begin_inset Formula 
\[
P(x|y)=\frac{1}{\sqrt{|2\pi\Sigma|}}\exp\left\{ -\frac{1}{2}\|Rx+Sy-d\|_{\Sigma}^{2}\right\} 
\]

\end_inset

where 
\begin_inset Formula $R$
\end_inset

 is square and upper-triangular.
 For every 
\emph on
GaussianConditional
\emph default
, we have the following 
\series bold
invariant
\series default
,
\begin_inset Formula 
\begin{equation}
\log P(x|y)=K_{gc}-E_{gc}(x,y),\label{eq:gc_invariant}
\end{equation}

\end_inset

with the 
\series bold
log-normalization constant
\series default
 
\begin_inset Formula $K_{gc}$
\end_inset

 equal to
\begin_inset Formula 
\begin{equation}
K_{gc}=\log\frac{1}{\sqrt{|2\pi\Sigma|}}\label{eq:log_constant}
\end{equation}

\end_inset

 and the 
\series bold
error
\series default
 
\begin_inset Formula $E_{gc}(x,y)$
\end_inset

 equal to the negative log-density, up to a constant: 
\begin_inset Formula 
\begin{equation}
E_{gc}(x,y)=\frac{1}{2}\|Rx+Sy-d\|_{\Sigma}^{2}.\label{eq:gc_error}
\end{equation}

\end_inset

.
\end_layout

\begin_layout Subsubsection*
GaussianMixture
\end_layout

\begin_layout Standard
A 
\emph on
GaussianMixture
\emph default
 (maybe to be renamed to 
\emph on
GaussianMixtureComponent
\emph default
) just indexes into a number of 
\emph on
GaussianConditional
\emph default
 instances, that are each properly normalized:
\end_layout

\begin_layout Standard
\begin_inset Formula 
\[
P(x|y,m)=P_{m}(x|y).
\]

\end_inset

We store one 
\emph on
GaussianConditional
\emph default
 
\begin_inset Formula $P_{m}(x|y)$
\end_inset

 for every possible assignment 
\begin_inset Formula $m$
\end_inset

 to a set of discrete variables.
 As 
\emph on
GaussianMixture
\emph default
 is a 
\emph on
Conditional
\emph default
, it needs to satisfy the a similar invariant to 
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:gc_invariant"
plural "false"
caps "false"
noprefix "false"

\end_inset

:
\begin_inset Formula 
\begin{equation}
\log P(x|y,m)=K_{gm}-E_{gm}(x,y,m).\label{eq:gm_invariant}
\end{equation}

\end_inset

If we take the log of 
\begin_inset Formula $P(x|y,m)$
\end_inset

 we get
\begin_inset Formula 
\begin{equation}
\log P(x|y,m)=\log P_{m}(x|y)=K_{gcm}-E_{gcm}(x,y).\label{eq:gm_log}
\end{equation}

\end_inset

Equating 
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:gm_invariant"
plural "false"
caps "false"
noprefix "false"

\end_inset

 and 
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:gm_log"
plural "false"
caps "false"
noprefix "false"

\end_inset

 we see that this can be achieved by defining the error 
\begin_inset Formula $E_{gm}(x,y,m)$
\end_inset

 as
\begin_inset Formula 
\begin{equation}
E_{gm}(x,y,m)=E_{gcm}(x,y)+K_{gm}-K_{gcm}\label{eq:gm_error}
\end{equation}

\end_inset

where choose 
\begin_inset Formula $K_{gm}=\max K_{gcm}$
\end_inset

, as then the error will always be positive.
\end_layout

\begin_layout Section
Hybrid Factors
\end_layout

\begin_layout Standard
In GTSAM, we typically condition on known measurements, and factors encode
 the resulting negative log-likelihood of the unknown variables 
\begin_inset Formula $y$
\end_inset

 given the measurements 
\begin_inset Formula $x$
\end_inset

.
 We review how a Gaussian conditional density is converted into a Gaussian
 factor, and then develop a hybrid version satisfying the correct invariants
 as well.
\end_layout

\begin_layout Subsubsection*
JacobianFactor
\end_layout

\begin_layout Standard
A 
\emph on
JacobianFactor
\emph default
 typically results from a 
\emph on
GaussianConditional
\emph default
 by having known values 
\begin_inset Formula $\bar{x}$
\end_inset

 for the 
\begin_inset Quotes eld
\end_inset

measurement
\begin_inset Quotes erd
\end_inset

 
\begin_inset Formula $x$
\end_inset

:
\begin_inset Formula 
\begin{equation}
L(y)\propto P(\bar{x}|y)\label{eq:likelihood}
\end{equation}

\end_inset

In GTSAM factors represent the negative log-likelihood 
\begin_inset Formula $E_{jf}(y)$
\end_inset

 and hence we have
\begin_inset Formula 
\[
E_{jf}(y)=-\log L(y)=C-\log P(\bar{x}|y),
\]

\end_inset

with 
\begin_inset Formula $C$
\end_inset

 the log of the proportionality constant in 
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:likelihood"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
 Substituting in 
\begin_inset Formula $\log P(\bar{x}|y)$
\end_inset

 from the invariant 
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:gc_invariant"
plural "false"
caps "false"
noprefix "false"

\end_inset

 we obtain
\begin_inset Formula 
\[
E_{jf}(y)=C-K_{gc}+E_{gc}(\bar{x},y).
\]

\end_inset

The 
\emph on
likelihood
\emph default
 function in 
\emph on
GaussianConditional
\emph default
 chooses 
\begin_inset Formula $C=K_{gc}$
\end_inset

, and the 
\emph on
JacobianFactor
\emph default
 does not store any constant; it just implements:
\begin_inset Formula 
\[
E_{jf}(y)=E_{gc}(\bar{x},y)=\frac{1}{2}\|R\bar{x}+Sy-d\|_{\Sigma}^{2}=\frac{1}{2}\|Ay-b\|_{\Sigma}^{2}
\]

\end_inset

with 
\begin_inset Formula $A=S$
\end_inset

 and 
\begin_inset Formula $b=d-R\bar{x}$
\end_inset

.
\end_layout

\begin_layout Subsubsection*
GaussianMixtureFactor
\end_layout

\begin_layout Standard
Analogously, a 
\emph on
GaussianMixtureFactor
\emph default
 typically results from a GaussianMixture by having known values 
\begin_inset Formula $\bar{x}$
\end_inset

 for the 
\begin_inset Quotes eld
\end_inset

measurement
\begin_inset Quotes erd
\end_inset

 
\begin_inset Formula $x$
\end_inset

:
\begin_inset Formula 
\[
L(y,m)\propto P(\bar{x}|y,m).
\]

\end_inset

We will similarly implement the negative log-likelihood 
\begin_inset Formula $E_{mf}(y,m)$
\end_inset

:
\begin_inset Formula 
\[
E_{mf}(y,m)=-\log L(y,m)=C-\log P(\bar{x}|y,m).
\]

\end_inset

Since we know the log-density from the invariant 
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:gm_invariant"
plural "false"
caps "false"
noprefix "false"

\end_inset

, we obtain
\begin_inset Formula 
\[
\log P(\bar{x}|y,m)=K_{gm}-E_{gm}(\bar{x},y,m),
\]

\end_inset

 and hence
\begin_inset Formula 
\[
E_{mf}(y,m)=C+E_{gm}(\bar{x},y,m)-K_{gm}.
\]

\end_inset

Substituting in 
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:gm_error"
plural "false"
caps "false"
noprefix "false"

\end_inset

 we finally have an expression where 
\begin_inset Formula $K_{gm}$
\end_inset

 canceled out, but we have a dependence on the individual component constants
 
\begin_inset Formula $K_{gcm}$
\end_inset

:
\begin_inset Formula 
\[
E_{mf}(y,m)=C+E_{gcm}(\bar{x},y)-K_{gcm}.
\]

\end_inset

Unfortunately, we can no longer choose 
\begin_inset Formula $C$
\end_inset

 independently from 
\begin_inset Formula $m$
\end_inset

 to make the constant disappear.
 There are two possibilities:
\end_layout

\begin_layout Enumerate
Implement likelihood to yield both a hybrid factor 
\emph on
and
\emph default
 a discrete factor.
\end_layout

\begin_layout Enumerate
Hide the constant inside the collection of JacobianFactor instances, which
 is the possibility we implement.
\end_layout

\begin_layout Standard
In either case, we implement the mixture factor 
\begin_inset Formula $E_{mf}(y,m)$
\end_inset

 as a set of 
\emph on
JacobianFactor
\emph default
 instances 
\begin_inset Formula $E_{mf}(y,m)$
\end_inset

, indexed by the discrete assignment 
\begin_inset Formula $m$
\end_inset

:
\begin_inset Formula 
\[
E_{mf}(y,m)=E_{jfm}(y)=\frac{1}{2}\|A_{m}y-b_{m}\|_{\Sigma_{mfm}}^{2}.
\]

\end_inset

In GTSAM, we define 
\begin_inset Formula $A_{m}$
\end_inset

 and 
\begin_inset Formula $b_{m}$
\end_inset

 strategically to make the 
\emph on
JacobianFactor
\emph default
 compute the constant, as well:
\begin_inset Formula 
\[
\frac{1}{2}\|A_{m}y-b_{m}\|_{\Sigma_{mfm}}^{2}=C+E_{gcm}(\bar{x},y)-K_{gcm}.
\]

\end_inset

Substituting in the definition 
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:gc_error"
plural "false"
caps "false"
noprefix "false"

\end_inset

 for 
\begin_inset Formula $E_{gcm}(\bar{x},y)$
\end_inset

 we need
\begin_inset Formula 
\[
\frac{1}{2}\|A_{m}y-b_{m}\|_{\Sigma_{mfm}}^{2}=C+\frac{1}{2}\|R_{m}\bar{x}+S_{m}y-d_{m}\|_{\Sigma_{m}}^{2}-K_{gcm}
\]

\end_inset

which can achieved by setting
\begin_inset Formula 
\[
A_{m}=\left[\begin{array}{c}
S_{m}\\
0
\end{array}\right],~b_{m}=\left[\begin{array}{c}
d_{m}-R_{m}\bar{x}\\
c_{m}
\end{array}\right],~\Sigma_{mfm}=\left[\begin{array}{cc}
\Sigma_{m}\\
 & 1
\end{array}\right]
\]

\end_inset

and setting the mode-dependent scalar 
\begin_inset Formula $c_{m}$
\end_inset

 such that 
\begin_inset Formula $c_{m}^{2}=C-K_{gcm}$
\end_inset

.
 This can be achieved by 
\begin_inset Formula $C=\max K_{gcm}=K_{gm}$
\end_inset

 and 
\begin_inset Formula $c_{m}=\sqrt{2(C-K_{gcm})}$
\end_inset

.
 Note that in case that all constants 
\begin_inset Formula $K_{gcm}$
\end_inset

 are equal, we can just use 
\begin_inset Formula $C=K_{gm}$
\end_inset

 and
\begin_inset Formula 
\[
A_{m}=S_{m},~b_{m}=d_{m}-R_{m}\bar{x},~\Sigma_{mfm}=\Sigma_{m}
\]

\end_inset

as before.
\end_layout

\begin_layout Standard
In summary, we have
\begin_inset Formula 
\begin{equation}
E_{mf}(y,m)=\frac{1}{2}\|A_{m}y-b_{m}\|_{\Sigma_{mfm}}^{2}=E_{gcm}(\bar{x},y)+K_{gm}-K_{gcm}.\label{eq:mf_invariant}
\end{equation}

\end_inset

which is identical to the GaussianMixture error 
\begin_inset CommandInset ref
LatexCommand eqref
reference "eq:gm_error"
plural "false"
caps "false"
noprefix "false"

\end_inset

.
\end_layout

\end_body
\end_document