# Inference engine i in c

The following C project contains the C source code and C examples used for inference engine i. Inference Engine I is a scripting language with syntax and operation based entirely upon the Lisp (and Prolog) paradigm.

The following C project contains the C source code and C examples used for a gradient. a nice circular and linear gradient

The following Matlab project contains the source code and Matlab examples used for jackknife a statistic.
Function JKNIFE computes values of user-defined scalar-, vector- or matrix-valued statistics in the original sample, and in n leave-one-out samples, where n is the number of rows in X.

The following Matlab project contains the source code and Matlab examples used for bootstrap geometric mean regression (bootstrap reduced majoraxis regression). .
The bootstrap is a way of estimating the variability of a statistic from a single data set by resampling it independently and with equal probabilities (Monte Carlo resampling).

The following Matlab project contains the source code and Matlab examples used for a neural network based dynamic forecasting model for trend impact analysis.
Trend Impact Analysis is a simple forecasting approach, yet powerful, within the Futures Studies paradigm.

The following Matlab project contains the source code and Matlab examples used for generate (wild) bootstrap samples .
Generate "Wild bootstrap" samples according to Mammen, E. (1993), "Bootstrap and Wild Bootstrap for High Dimensional Linear Models", Annals of Statistics 21.
Test it by computing standard error

The following Matlab project contains the source code and Matlab examples used for orthogonal least squares algorithm for rbf networks.
The code implements the algorithm as given in Chen et.

The following Matlab project contains the source code and Matlab examples used for mcmc -- markov chain monte carlo tools .
MCMC -- Markov Chain Monte Carlo Tools Copyright (c) 1998, Harvard University.

The following Matlab project contains the source code and Matlab examples used for simple perceptron.
Perceptron is an algorithm for supervised classification of an input into one of several possible non-binary outputs. This mfile is a simple type of perceptron to who like to learn about the perceptron type of artificial neural networks

Particle filters or Sequential Monte Carlo (SMC) methods are a set of on-line posterior density estimation algorithms that estimate the posterior density of the state-space by directly implementing the Bayesian recursion equations.

The following matlab project contains the source code and matlab examples used for particle filter.

The following Matlab project contains the source code and Matlab examples used for resampling methods for particle filtering.
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Particle filter resampling
Author: Jose Luis Blanco
Web: http://www.

The following Matlab project contains the source code and Matlab examples used for improved feedforward neural networks using psogsa.
This work utilizes a hybrid of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) called PSOGSA for training Feedforward Neural Networks (FNNs).

The following Matlab project contains the source code and Matlab examples used for character recognition using neural networks.
Character Recognition Using Neural Networks
Steps to use this GUI.

The following Matlab project contains the source code and Matlab examples used for gui for cellular neural network.
Cellular neural networks (CNN) are a parallel computing paradigm similar to neural networks, with the difference that communication is allowed between neighbouring units only.

The following Matlab project contains the source code and Matlab examples used for neural network rbf regression.
RBF based Neural Network Regression points

The following Matlab project contains the source code and Matlab examples used for mmgdx a maximum margin training method for neural networks.
The large classification margin is the most usual approach to achieve good generalization.

The following Matlab project contains the source code and Matlab examples used for neural networks for localized atmospheric density forecasting.
The following cases are presented
1.

The following source code and examples are used for sparse hierarchical clustering and sparse k-means clustering that Implements the sparse clustering methods of Witten and Tibshirani (2010): "A framework for feature selection in clustering".

The following source code and examples are used for Convex Clustering Methods and Clustering Indexes that including K-means algorithm, On-line Update algorithm (Hard Competitive Learning) and Neural Gas algorithm (Soft Competitive Learning).

The following source code and examples are used for Robust Loss Development Using MCMC that are Loss Development for Insurance Triangles.

The following source code and examples are used for MCMC plots.

This package provides two generic functions for performing Markov chain sampling in a naive way for a user-defined target distribution, which involves only continuous variables.

This package computes the bootstrap goodness-of-fit test for the generalized Pareto distribution by Villasenor-Alva and Gonzalez-Estrada

The following package is about Learning Bayesian Inference. it may be helpful in learning the basic tenets of Bayesian statistical inference..

The following R package and source code is used for Trimmed k-means clustering.