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The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications. Intelligent Agents. Solvin...

Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, re...

Understanding the evolutionary history of living organisms is a central problem in biology. Until recently the ability to infer evolutionary relationships was limited by the amount of DNA sequence data available, but new...

Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains ...

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

SUMMARY: The program MRBAYES performs Bayesian inference of phylogeny using a variant of Markov chain Monte Carlo. AVAILABILITY: MRBAYES, including the source code, documentation, sample data files, and an executable, is...

Abstract Summary: MrBayes 3 performs Bayesian phylogenetic analysis combining information from different data partitions or subsets evolving under different stochastic evolutionary models. This allows the user to analyze...

Subjects / keywords: Cluster analysis; Inference; Computational biology; Computer science; Gene regulatory network; Artificial intelligence; Biology; Genetics; Gene; Gene expression

Subjects / keywords: Statistical inference; Inference; Computer science; Artificial intelligence; Statistics; Mathematics

Praise for the First Edition: 'I recommend this book, without hesitation, as either a reference or course text...Wilks' excellent book provides a thorough base in applied statistical methods for atmospheric sciences' - B...

Covers supervised learning (prediction) to unsupervised learning. This book contains topics including neural networks, support vector machines, classification trees and boosting

Subjects / keywords: Statistical inference; Inference; Statistical learning; Computer science; Machine learning; Artificial intelligence; Data mining; Statistics; Mathematics

Subjects / keywords: Thresholding; Smoothing; Voxel; Artificial intelligence; Computer science; Cluster (spacecraft); Sensitivity (control systems); Pattern recognition (psychology); Scan statistic; Inference; Algorithm; Image (mathematics)

We show how to use "complementary priors" to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast...

In object detection, an intersection over union (IoU) threshold is required to define positives and negatives. An object detector, trained with low IoU threshold, e.g. 0.5, usually produces noisy detections. However, det...

A first step in identifying the content of a document is determining which topics that document addresses. We describe a generative model for documents, introduced by Blei, Ng, and Jordan [Blei, D. M., Ng, A. Y. & Jordan...

Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, improve on the previous best result in semant...

Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segme...

Subjects / keywords: Recurrent neural network; Computer science; Inference; Artificial intelligence; Notation; Machine learning; Artificial neural network; Arithmetic; Mathematics

Abstract We generalize the method proposed by Gelman and Rubin (1992a) for monitoring the convergence of iterative simulations by comparing between and within variances of multiple chains, in order to obtain a family of ...

Subjects / keywords: Generalized linear mixed model; Inference; Statistical inference; Computer science; Ecology; Evolutionary ecology; Econometrics; Mathematics; Statistics; Machine learning; Artificial intelligence; Biology

By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulat...

Subjects / keywords: Variety (cybernetics); Computer science; Artificial intelligence; Inference; Machine learning; ID3; Decision tree; Management science; Decision tree learning; Engineering

Several variants of the long short-term memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995. In recent years, these networks have become the state-of-the-art models for...