Foreword
The four case studies that follow each have a number of common features. They each illustrate the birth of an idea and show how that idea can be realised into a marketable product. Each case study deals with engineering design and development issues and each highlights the importance of developing sound marketing strategies including market research. The importance of appropriate support mechanisms for young entrepreneurs is also covered. The case studies illustrate how successful entrepreneurs deploy a range of entrepreneurial skills and know-how. Above all, the entrepreneurs are seen to have the capacity to innovate and exercise vision.
We are grateful to Liz Read, Development Manager for Enterprise and Entrepreneurship (Students) at Coventry University for providing these case studies.
Bold index entries have corresponding pages in the main body of the
guide. The remaining index entries are followed by a list of
appropriate page references in the alphabetical reference sections,
given in order of importance.
From helping to assess the value of new medical treatments to evaluating the
factors that affect our opinions and behaviors, analysts today are finding
myriad uses for categorical data methods. In this book we introduce these
methods and the theory behind them.
Statistical methods for categorical responses were late in gaining the level
of sophistication achieved early in the twentieth century by methods for
continuous responses. Despite influential work around 1900 by the British
statistician Karl Pearson, relatively little development of models for categorical
responses occurred until the 1960s. In this book we describe the early
fundamental work that still has importance today but place primary emphasis
on more recent modeling approaches. Before outlining
In the field of biometrics, palmprint is a novel but promising technology. Limited work has been reported on palmprint identification and verification, despite the importance of palmprint features. There are many unique features in a palmprint image that can be used for personal identification. Principal lines, wrinkles, ridges, minutiae points, singular points, and texture are regarded as useful features for palmprint representation.
Sequential Monte Carlo without Likelihoods
粒子濾波不用似然函數的情況下
本文摘要:Recent new methods in Bayesian simulation have provided ways of evaluating posterior distributions
in the presence of analytically or computationally intractable likelihood functions.
Despite representing a substantial methodological advance, existing methods based on rejection
sampling or Markov chain Monte Carlo can be highly inefficient, and accordingly
require far more iterations than may be practical to implement. Here we propose a sequential
Monte Carlo sampler that convincingly overcomes these inefficiencies. We demonstrate
its implementation through an epidemiological study of the transmission rate of tuberculosis.
The package includes 3 Matlab-interfaces to the c-code:
1. inference.m
An interface to the full inference package, includes several methods for
approximate inference: Loopy Belief Propagation, Generalized Belief
Propagation, Mean-Field approximation, and 4 monte-carlo sampling methods
(Metropolis, Gibbs, Wolff, Swendsen-Wang).
Use "help inference" from Matlab to see all options for usage.
2. gbp_preprocess.m and gbp.m
These 2 interfaces split Generalized Belief Propagation into the pre-process
stage (gbp_preprocess.m) and the inference stage (gbp.m), so the user may use
only one of them, or changing some parameters in between.
Use "help gbp_preprocess" and "help gbp" from Matlab.
3. simulatedAnnealing.m
An interface to the simulated-annealing c-code. This code uses Metropolis
sampling method, the same one used for inference.
Use "help simulatedAnnealing" from Matlab.
selects the mux channel and configures the MAX197 for
second write pulse, written with ACQMOD = 0, termi-
either unipolar or bipolar input range. A write pulse (WR
nates acquisition and starts conversion on WR°Os risin
+ CS) can either start an acquisition interval or initiate a
edge (Figure 6). However, if the second control byte
combined acquisition plus conversion. The sampling
contains ACQMOD = 1, an indefinite acquisition interval
interval occurs at the end of the acquisition interval.
is restarted.
The ACQMOD bit in the input control byte offer
selects the mux channel and configures the MAX197 for
second write pulse, written with ACQMOD = 0, termi-
either unipolar or bipolar input range. A write pulse (WR
nates acquisition and starts conversion on WR°Os risin
+ CS) can either start an acquisition interval or initiate a
edge (Figure 6). However, if the second control byte
combined acquisition plus conversion. The sampling
contains ACQMOD = 1, an indefinite acquisition interval
interval occurs at the end of the acquisition interval.
is restarted.
The ACQMOD bit in the input control byte offer
selects the mux channel and configures the MAX197 for
second write pulse, written with ACQMOD = 0, termi-
either unipolar or bipolar input range. A write pulse (WR
nates acquisition and starts conversion on WR°Os risin
is restarted.
The ACQMOD bit in the input control byte offer+ CS) can either start an acquisition interval or initiate a
edge (Figure 6). However, if the second control byte
combined acquisition plus conversion. The sampling
contains ACQMOD = 1, an indefinite acquisition interval
interval occurs at the end of the acquisition interval.
