The Netlab toolbox is designed to provide the central tools necessary for the simulation of theoretically well founded
neural network algorithms and related MODELS for use in teaching, research and applications development. It contains
many techniques which are not yet available in standard neural network simulation packages
The software implements particle filtering and Rao Blackwellised particle filtering for conditionally Gaussian MODELS. The RB algorithm can be interpreted as an efficient stochastic mixture of Kalman filters. The software also includes efficient state-of-the-art resampling routines. These are generic and suitable for any application.
一個(gè)非常好的時(shí)間序列工具箱，詳細(xì)使用說明見P. M. T. Broersen, Automatic Spectral Analysis with Time Series MODELS, IEEE Transactions on Instrumentation and Measurement, Vol. 51, No. 2, April 2002, pp. 211-216.
ReBEL is a Matlabtoolkit of functions and scripts, designed to
facilitate sequential Bayesian inference (estimation) in general state
space MODELS. This software consolidates research on new methods for
recursive Bayesian estimation and Kalman filtering by Rudolph van der
Merwe and Eric A. Wan. The code is developed and maintained by Rudolph
van der Merwe at the OGI School of Science & Engineering at OHSU
(Oregon Health & Science University).
CHMMBOX, version 1.2, Iead Rezek, Oxford University, Feb 2001
Matlab toolbox for max. aposteriori estimation of two chain
Coupled Hidden Markov MODELS.
Demostration of example 6.2: Constrained Receding Horizon Control
Example retired from the book: Receding Horizon Control - Model Predictive Control for State MODELS
published on 2007-03-28
The function conload takes a dataset and a model (PCA, PLS, PARAFAC etc.) and calculates congruence loadings which is the extension of correlation loadings to uncentered and multi-way MODELS
The Staged Event-Driven Architecture (SEDA) is a new design for building scalable Internet services. SEDA has three major goals:
To support massive concurrency, on the order of tens of thousands of clients per node
To exhibit robust performance under wide variations in load and,
To simplify the design of complex Internet services.
SEDA decomposes a complex, event-driven application into a set of stages connected by queues. This design avoids the high overhead associated with thread-based concurrency MODELS, and decouples event and thread scheduling from application logic. SEDA enables services to be well-conditioned to load, preventing resources from being overcommitted when demand exceeds service capacity. Decomposing services into a set of stages also enables modularity and code reuse, as well as the development of debugging tools for complex event-driven applications.
