Testing independencies in Bayesian networks (BNs) is a fundamental task in probabilistic reasoning. In this paper, we propose inaugural-separation (i-separation) as a new method for testing independencies in BNs. We establish the correctness of i-separation. Our method has several theoretical and practical advantages. There are at least five ways in which i-separation is simpler than d-separation, the classical method for testing independencies in BNs, of which the most important is that "blocking" works in an intuitive fashion. In practice, our empirical evaluation shows that i-separation tends to be faster than d-separation in large BNs.

We develop a novel propositionalization approach to unsupervised outlier detection for multi-relational data. Propositionalization summarizes the information from multi-relational data, that are typically stored in multiple tables, in a single data table. The columns in the data table represent conjunctive relational features that are learned from the data. An advantage of propositionalization is that it facilitates applying the many previous outlier detection methods that were designed for single-table data. We show that conjunctive features for outlier detection can be learned from data using statistical-relational methods. Specifically, we apply Markov Logic Network structure learning. Compared to baseline propositionalization methods, Markov Logic propositionalization produces the most compact data tables, whose attributes capture the most complex multi-relational correlations. We apply three representative outlier detection methods LOF, KNN, OutRank to the data tables constructed by propositionalization.

We propose Simple Propagation (SP) as a new join tree propagation algorithm for exact inference in discrete Bayesian networks. We establish the correctness of SP. The striking feature of SP is that its message construction exploits the factorization of potentials at a sending node, but without the overhead of building and examining graphs as done in Lazy Propagation (LP). Experimental results on numerous benchmark Bayesian networks show that SP is often faster than LP.

When a severe nuclear power plant accident occurs, plant operators rely on Severe Accident Management Guidelines (SAMGs). However, current SAMGs are limited in scope and depth. The plant operators must work to mitigate the accident with limited experience and guidance for the situation. The SMART (Safely Managing Accidental Reactor Transients) procedures framework aims to fill the need for detailed guidance by creating a comprehensive probabilistic model, using a Dynamic Bayesian Network, to aid in the diagnosis of the reactor’s state. In this paper, we explore the viability of the proposed SMART proceedures approach by building a prototype Bayesian network that allows for the diagnosis of two types of accidents based on a comprehensive data set. We use Kullback-Leibler (K-L) divergence to gauge the relative importance of each of the plant’s parameters. We compare accuracy and F-score measures across four different Bayesian networks: a baseline network that ignores observation variables, a network that ignores data from the observation variable with the highest K-L score, a network that ignores data from the variable with the lowest K-L score, and finally a network that includes all observation variable data. We conclude with an interpretation of these results for SMART procedures.

One is going to give a historical overview about GoogleBrain and analyse the pattern, then we will conculde our finding about the current sitation and future changes in search engine algorithm. Back in 2006 there were some interests in implementing artificial intelligence in Google search engine algorithm. A few years later in 2014, GoogleBrain was established after acquisition of DeepMind, a British artificial intelligence company which was founded in 2010. They worked on how to play video games based on machine learning and artificial neural networks (ANNs). The smart artificial intelligence revolution can recognize patterns in digital representations of sounds, images and data.

A general method of building a predictive model requires least square estimation at first. Then we need work on the residuals, find the confidence interval of parameters and test how well the model fits the data which are based on the normally distributed assumption of the residuals (or noises). But unfortunately the assumption is not guaranteed. Most of the time, you will have a graph of residuals that looks like another distribution rather than the normal. At this moment you could add one more factor term to your model so as to filter out the non-normal distributed noise, and then calculate the LSE again.

Is it possible to classify and predict (yes, predict!) if market trends will be bullish, bear or ranged by using a method called "naïve" and based on something as simple as Bayes' theorem is? Let's see! Our main objective is to explore techniques of machine learning that can help us not only to label series in a posteriori analysis, but also to predict to which class a new value given of the serie belongs to. The Naïve Bayesian Classifier is a supervised learning method of machine learning as well as a statistical method for classification. Although this method is including in its name a word as rare as "naïve" is, it will be our tool chosen to predict different trends of a market represented by an index. Bayesian classification provides practical learning algorithms where prior knowledge and observed data can be combined.

This article aims to provide the basic knowledge of how to recognize snacks by using Python and SimpleCV. Readers will gain practical programming knowledge via experimentation with the Python scripts included in the Snack Classifier open source project. To illustrate with a snacks recognition app, the Snack Watcher watches any snacks present on the snack table. For Snack Watcher to determine if there was an interesting event, it needs to process the image into a set of image "Blobs". For each "Blob", Snack Watcher compares the "Blob" with it's previous state to determine if the "Blob" was added, removed or stationary.

Energy is a limited resource which has to be managed wisely, taking into account both supply-demand matching and capacity constraints in the distribution grid. One aspect of the smart energy management at the building level is given by the problem of real-time detection of flexible demand available. In this paper we propose the use of energy disaggregation techniques to perform this task. Firstly, we investigate the use of existing classification methods to perform energy disaggregation. A comparison is performed between four classifiers, namely Naive Bayes, k-Nearest Neighbors, Support Vector Machine and AdaBoost. Secondly, we propose the use of Restricted Boltzmann Machine to automatically perform feature extraction. The extracted features are then used as inputs to the four classifiers and consequently shown to improve their accuracy. The efficiency of our approach is demonstrated on a real database consisting of detailed appliance-level measurements with high temporal resolution, which has been used for energy disaggregation in previous studies, namely the REDD. The results show robustness and good generalization capabilities to newly presented buildings with at least 96% accuracy.

We consider a distributed learning setup where a network of agents sequentially access realizations of a set of random variables with unknown distributions. The network objective is to find a parametrized distribution that best describes their joint observations in the sense of the Kullback-Leibler divergence. Apart from recent efforts in the literature, we analyze the case of countably many hypotheses and the case of a continuum of hypotheses. We provide non-asymptotic bounds for the concentration rate of the agents' beliefs around the correct hypothesis in terms of the number of agents, the network parameters, and the learning abilities of the agents. Additionally, we provide a novel motivation for a general set of distributed Non-Bayesian update rules as instances of the distributed stochastic mirror descent algorithm.