From the Publisher:
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.

/pdf/neural-networks-for-pattern-recognition-34puigiau1.pdf

Neural networks for pattern recognition

The NMRPipe system is a UNIX software environment of processing, graphics, and analysis tools designed to meet current routine and research-oriented multidimensional processing requirements, and to anticipate and accommodate future demands and developments. The system is based on UNIX pipes, which allow programs running simultaneously to exchange streams of data under user control. In an NMRPipe processing scheme, a stream of spectral data flows through a pipeline of processing programs, each of which performs one component of the overall scheme, such as Fourier transformation or linear prediction. Complete multidimensional processing schemes are constructed as simple UNIX shell scripts. The processing modules themselves maintain and exploit accurate records of data sizes, detection modes, and calibration information in all dimensions, so that schemes can be constructed without the need to explicitly define or anticipate data sizes or storage details of real and imaginary channels during processing. The asynchronous pipeline scheme provides other substantial advantages, including high flexibility, favorable processing speeds, choice of both all-in-memory and disk-bound processing, easy adaptation to different data formats, simpler software development and maintenance, and the ability to distribute processing tasks on multi-CPU computers and computer networks.

NMRPipe: a multidimensional spectral processing system based on UNIX pipes

https://www.cs.princeton.edu/~schapire/papers/ecolmod.pdf

Maximum entropy modeling of species geographic distributions

Inference of macromolecular assemblies from crystalline state.

Fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.

/pdf/information-theory-inference-and-learning-algorithms-gbv6i2zu22.pdf

Information Theory, Inference and Learning Algorithms

1. The Basics 2. Parameter Estimation I 3. Parameter Estimation II 4. Model Selection 5. Assigning Probabilities 6. Non-parametric Estimation 7. Experimental Design 8. Least-Squares Extensions 9. Nested Sampling 10. Quantification Appendices Bibliography

Data analysis : a Bayesian tutorial

/pdf/maximum-entropy-and-bayesian-methods-499wl7d1x8.pdf

Maximum Entropy and Bayesian Methods

Le maximum d'entropie est une technique optimale de reconstruction d'image, largement applicable en astronomie. On presente un algorithme capable de generer des images provenant d'une large variete de types de donnees, par cette technique

Maximum entropy image reconstruction: general algorithm

Maximum entropy has proved to be an enormously powerful tool for reconstructing images from many types of data. It has a privileged position as the only consistent method for combining different data into a single image. It has been used most spectacularly in radio astronomical interferometry, where it deals routinely with images of up to a million pixels, and high dynamic range. We also give examples of optical deconvolutions and tomographic reconstructions to illustrate the generality of application and the quality of maximum entropy images. Some types of data, a such as Fourier intensitites, are inadepquate in themselves to produce a good image. The maximum entropy method allows us to incorporate extra, Prior knowledge about the object being imaged, and we give examples of this technique being used in specectroscopy. The nonlinearities inherent in the a state-of-the-art example of `blind' deconvolution in which an unknown object is blurred with an unknown point-spread-function: maximum entropy can recover both.

Maximum entropy method in image processing

This paper presents a fully Bayesian derivation of maximum entropy image reconstruction. The argument repeatedly goes from the particular to the general, in that if there are general theories then they must apply to special cases. Two such special cases, formalised as the “Cox axioms “, lead to the well-known fact that Bayesian probability theory is the only consistent language of inference. Further cases, formalised as the axioms of maximum entropy, show that the prior probability distribution for any positive, additive distribution must be monotonic in the entropy. Finally, a quantified special case shows that this monotonic function must be the exponential, leaving only a single dimensional scaling factor to be determined a posteriori. Many types of distribution, including probability distributions themselves, are positive and additive, so the entropy exponential is very general.

John Skilling

Papers

Data analysis : a Bayesian tutorial

Maximum Entropy and Bayesian Methods

Maximum entropy image reconstruction: general algorithm

Maximum entropy method in image processing

Classic Maximum Entropy