This paper introduces the factored conditional filter, a new filtering
algorithm for simultaneously tracking states and estimating parameters in
high-dimensional state spaces. The conditional nature of the algorithm is used
to estimate parameters and the factored nature is used to decompose the state
space into low-dimensional subspaces in such a way that filtering on these
subspaces gives distributions whose product is a good approximation to the
distribution on the entire state space. The conditions for successful
application of the algorithm are that observations be available at the subspace
level and that the transition model can be factored into local transition
models that are approximately confined to the subspaces; these conditions are
widely satisfied in computer science, engineering, and geophysical filtering
applications. We give experimental results on tracking epidemics and estimating
parameters in large contact networks that show the effectiveness of our
approach.

本文介绍了分解条件过滤器的新型滤波算法，用于同时跟踪状态和估计高维状态空间中的参数，是一种用于计算机科学、工程和地球物理过滤应用的有效工具。

高维空间中状态跟踪和参数估计的因子条件滤波

Factored Conditional Filtering: Tracking States and Estimating Parameters in High-Dimensional Spaces

Object tracking is a key aspect in many applications such as augmented
reality in medicine (e.g. tracking a surgical instrument) or robotics. Squared
planar markers have become popular tools for tracking since their pose can be
estimated from their four corners. While using a single marker and a single
camera limits the working area considerably, using multiple markers attached to
an object requires estimating their relative position, which is not trivial,
for high accuracy tracking. Likewise, using multiple cameras requires
estimating their extrinsic parameters, also a tedious process that must be
repeated whenever a camera is moved.
This work proposes a novel method to simultaneously solve the above-mentioned
problems. From a video sequence showing a rigid set of planar markers recorded
from multiple cameras, the proposed method is able to automatically obtain the
three-dimensional configuration of the markers, the extrinsic parameters of the
cameras, and the relative pose between the markers and the cameras at each
frame. Our experiments show that our approach can obtain highly accurate
results for estimating these parameters using low resolution cameras.
Once the parameters are obtained, tracking of the object can be done in real
time with a low computational cost. The proposed method is a step forward in
the development of cost-effective solutions for object tracking.

本文提出了一种新的方法，同时解决了使用固定平面标记来进行多相机三维物体跟踪所面临的问题，能够在低分辨率摄像头下获得高精度的跟踪结果，从而在实现物体实时跟踪的同时，大大降低了计算成本。

使用平面正方形标记同时进行多视角相机姿态估计和物体跟踪

Simultaneous Multi-View Camera Pose Estimation and Object Tracking with  Square Planar Markers

Implicit probabilistic models are models defined naturally in terms of a
sampling procedure and often induces a likelihood function that cannot be
expressed explicitly. We develop a simple method for estimating parameters in
implicit models that does not require knowledge of the form of the likelihood
function or any derived quantities, but can be shown to be equivalent to
maximizing likelihood under some conditions. Our result holds in the
non-asymptotic parametric setting, where both the capacity of the model and the
number of data examples are finite. We also demonstrate encouraging
experimental results.

我们开发了一种简单的方法，用于估计隐式模型中的参数，无需了解似然函数或任何导出量的形式，但在某些条件下可以证明等价于最大化似然函数。此结果适用于参数有限且数据示例数量有限的非渐近参数设置，并展示了令人鼓舞的实验结果。