股票交易模擬器
A Stock Exchange simulator to show timers and randon number generators work together. A cool simulation for anyone who might think about playing the stocks and spending money and get a general IDEA how the system works.
In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discretedata
linear filtering problem [Kalman60]. Since that time, due in large part to advances in digital
computing, the
Kalman filter
has been the subject of extensive research and application,
particularly in the area of autonomous or assisted navigation. A very “friendly” introduction to the
general IDEA of the Kalman filter can be found in Chapter 1 of [Maybeck79], while a more complete
introductory discussion can be found in [Sorenson70], which also contains some interesting
historical narrative.
This document contains a general overview in the first few sections as well as a more detailed reference in later sections for SVMpython. If you re already familiar with SVMpython, it s possible to get a pretty good IDEA of how to use the package merely by browsing through svmstruct.py and multiclass.py. This document provides a more in depth view of how to use the package.
Note that this is not a conversion of SVMstruct to Python. It is merely an embedding of Python in existing C code. All code other than the user implemented API functions is still in C, including optimization.
The need for accurate monitoring and analysis of sequential data arises in many scientic, industrial
and nancial problems. Although the Kalman lter is effective in the linear-Gaussian
case, new methods of dealing with sequential data are required with non-standard models.
Recently, there has been renewed interest in simulation-based techniques. The basic IDEA behind
these techniques is that the current state of knowledge is encapsulated in a representative
sample from the appropriate posterior distribution. As time goes on, the sample evolves and
adapts recursively in accordance with newly acquired data. We give a critical review of recent
developments, by reference to oil well monitoring, ion channel monitoring and tracking
problems, and propose some alternative algorithms that avoid the weaknesses of the current
methods.
ICA介紹課件。There has been a wide discussion about the application of Independence Component Analysis (ICA) in Signal Processing, Neural Computation and Finance, first introduced as a novel tool to separate blind sources in a mixed signal. The Basic IDEA of ICA is to reconstruct from observation sequences the hypothesized independent original sequences
