Defines the main features of personality disorders. Describes the three clusters of personality disorders. Describes the 10 personality disorders categorized in DSM-5tr. Identifies models of, and effective treatments for, personality disorders

Describes the symptoms of intermittent explosive disorder, kleptomania, and pyromania. Explains the models and related treatments for impulse control disorders. Describes the symptoms of oppositional defiant disorder and conduct disorder.

Convolutional neural networks (CNNs) are prevalent in computer vision, image, speech, and language processing applications, where they have been successfully applied to perform classification tasks at high accuracy rates. One of their main attractions is the ability to operate directly on raw input signals, such as images, and to extract salient features automatically from the raw data. The designer does not need to worry about which features to select to drive the classification process.

When the training data $\{\text{γ}\left(n\right),h_n\}$ is linearly separable, there will exist many separating hyperplanes that can discriminate the data into two classes. Some of the techniques we described in the previous chapters, such as logistic regression and perceptron, are able to find such separating hyperplanes.

What do we know about the universe? And why? In particular, what are the categories of knowledge, the principles by which we recognize phenomena – for example, phenomena of perception – and by which we imagine things and situations in imaginary universes? How much of that knowledge is somehow reflected in, or reflects, language and/or discourse? How, then, do we communicate and share that knowledge? How much of the knowledge that language codes is universal in some sense, and how much is socio-historically specific? To answer these questions we need a revision of the account of language as handed down to us by Western philosophy, arguably as early as Plato’s Cratylus and continuing into the post-Enlightenment theories of language.

We develop a sequential version of the importance sampling technique from Chapter 33 in order to respond to streaming data, thus leading to a sequential Monte Carlo solution. The algorithm will lead to the important class of particle filters. This chapter presents the basic data model and the main construction that enables recursive inference. Many of the inference and learning methods in subsequent chapters will possess a recursive structure, which is a fundamental property to enable them to continually learn in response to the arrival of sequential data measurements. Particle filters are particularly well suited for scenarios involving nonlinear models and non-Gaussian signals, and they have found applications in a wide range of areas where these two features (nonlinearity and non-Gaussianity) are prevalent, including in guidance and control, robot localization, visual tracking of objects, and finance.

The optimal Bayes classifier (52.8) requires knowledge of the conditional probability distribution $\mathbb{P}\left(\mathit{r}=r|\mathit{h}=h\right)$, which is generally unavailable. In this and the next few chapters, we describe data‐based generative methods that approximate the joint probability distribution $f_{\mathit{r},\mathit{h}}\left(r,h\right)$, or its components $\mathbb{P}\left(r=r\right)$ and $f_{h|r}\left(h|r\right)$, directly from the data.

We described several data-based methods for inference and learning in the previous chapters. These methods operate directly on the data to arrive at classification or inference decisions. One key challenge these methods face is that the available training data need not provide sufficient representation for the sample space.