Theory Of Inference Research Articles

Active inference unifies intentional and conflict-resolution imperatives of motor control.

The field of motor control has long focused on the achievement of external goals through action (e.g., reaching and grasping objects). However, recent studies in conditions of multisensory conflict, such as when a subject experiences the rubber hand illusion or embodies an avatar in virtual reality, reveal the presence of unconscious movements that are not goal-directed, but rather aim at resolving multisensory conflicts; for example, by aligning the position of a person’s arm with that of an embodied avatar. This second, conflict-resolution imperative of movement control did not emerge in classical studies of motor adaptation and online corrections, which did not allow movements to reduce the conflicts; and has been largely ignored so far in formal theories. Here, we propose a model of movement control grounded in the theory of active inference that integrates intentional and conflict-resolution imperatives. We present three simulations showing that the active inference model is able to characterize movements guided by the intention to achieve an external goal, by the necessity to resolve multisensory conflict, or both. Furthermore, our simulations reveal a fundamental difference between the (active) inference underlying intentional and conflict-resolution imperatives by showing that it is driven by two different (model and sensory) kinds of prediction errors. Finally, our simulations show that when movement is only guided by conflict resolution, the model incorrectly infers that is velocity is zero, as if it was not moving. This result suggests a novel speculative explanation for the fact that people are unaware of their subtle compensatory movements to avoid multisensory conflict. Furthermore, it can potentially help shed light on deficits of motor awareness that arise in psychopathological conditions.

Large dimensional latent factor modeling with missing observations and applications to causal inference

This paper develops the inferential theory for latent factor models estimated from large dimensional panel data with missing observations. We propose an easy-to-use all-purpose estimator for a latent factor model by applying principal component analysis to an adjusted covariance matrix estimated from partially observed panel data. We derive the asymptotic distribution for the estimated factors, loadings and the imputed values under an approximate factor model and general missing patterns. The key application is to estimate counterfactual outcomes in causal inference from panel data. The unobserved control group is modeled as missing values, which are inferred from the latent factor model. The inferential theory for the imputed values allows us to test for individual treatment effects at any time under general adoption patterns where the units can be affected by unobserved factors.

Functionalism about inference

ABSTRACT Inferences are familiar movements of thought, but despite important recent work on the topic, we do not yet have a fully satisfying theory of inference. Here I provide a functionalist theory of inference. I argue that the functionalist framework allows us the flexibility to meet various demands on a theory of inference that have been proposed (such as that it must explain inferential Moorean phenomena and epistemological ‘taking’). While also allowing us to compare, contrast, adapt, and combine features of extant theories of inference into one unified theory. In fleshing out the inference role, I also criticize the common assumption that inference requires rule-following.

An Active Inference Account of Touch and Verbal Communication in Therapy.

This paper offers theoretical explanations for why “guided touch” or manual touch with verbal communication can be an effective way of treating the body (e.g., chronic pain) and the mind (e.g., emotional disorders). The active inference theory suggests that chronic pain and emotional disorders can be attributed to distorted and exaggerated patterns of interoceptive and proprioceptive inference. We propose that the nature of active inference is abductive. As such, to rectify aberrant active inference processes, we should change the “Rule” of abduction, or the “prior beliefs” entailed by a patient’s generative model. This means pre-existing generative models should be replaced with new models. To facilitate such replacement—or updating—the present treatment proposes that we should weaken prior beliefs, especially the one at the top level of hierarchical generative models, thereby altering the sense of agency, and redeploying attention. Then, a new prior belief can be installed through inner communication along with manual touch. The present paper proposes several hypotheses for possible experimental studies. If touch with verbal guidance is proven to be effective, this would demonstrate the relevance of active inference and the implicit prediction model at a behavioral level. Furthermore, it would open new possibilities of employing inner communication interventions, including self-talk training, for a wide range of psychological and physical therapies.

Maximum entropy derived and generalized under idempotent probability to address Bayes-frequentist uncertainty and model revision uncertainty: An information-theoretic semantics for possibility theory

Typical statistical methods of data analysis only handle determinate uncertainty, the type of uncertainty that can be modeled under the Bayesian or confidence theories of inference. An example of indeterminate uncertainty is uncertainty about whether the Bayesian theory or the frequentist theory is better suited to the problem at hand. Another example is uncertainty about how to modify a Bayesian model upon learning that its prior is inadequate. Both problems of indeterminate uncertainty have solutions under the proposed framework. The framework is based on an information-theoretic definition of an incoherence function to be minimized. It generalizes the principle of choosing an estimate that minimizes the reverse relative entropy between it and a previous posterior distribution such as a confidence distribution. The simplest form of the incoherence function, called the incoherence distribution, is a min-plus probability distribution, which is equivalent to a possibility distribution rather than a measure-theoretic probability distribution. A simple case of minimizing the incoherence leads to a generalization of minimizing relative entropy and thus of maximizing entropy. The framework of minimum incoherence is applied to problems of Bayesian-confidence uncertainty and to parallel problems of indeterminate uncertainty about model revision.

The Role of Conversational Implicature in Daily Conversations – What Matters, Content or Context?

In daily conversation, sometimes dialogue includes terms that vary entirely from the common phrases. From a linguistic perspective, the conversational implicatures are the speaker's intended meaning of the utterance. Conversation implications are the specific conversations between the speaker and the receiver by following the communications principles. It is the most significant component that has undergone argumentation in conversation theory. Grice's theory of dialogue inference is the possibility of providing meaning to the literal. In other words, people apply certain cooperative principles to communicate cooperatively. Thus, conversational implicatures have become one of the top research areas in pragmatics. This paper intends to explore the importance of conversational implicatures in day-to-day conversations in various contexts. It focuses on certain dialogues collected and integrated from the routine conversation. The outcome revealed that context plays a vital role in interpreting utterances. Therefore, there is no possibility of a complete correspondence of one utterance to one context, which shows the conversational implicature cannot be context-independent. In addition, dialogues were classified into Generalized, Scalar, and Particular conversational implicatures.

Inference in latent factor regression with clusterable features

Regression models, in which the observed features X∈Rp and the response Y∈R depend, jointly, on a lower dimensional, unobserved, latent vector Z∈RK, with K≪p, are popular in a large array of applications, and mainly used for predicting a response from correlated features. In contrast, methodology and theory for inference on the regression coefficient β∈RK relating Y to Z are scarce, since typically the un-observable factor Z is hard to interpret. Furthermore, the determination of the asymptotic variance of an estimator of β is a long-standing problem, with solutions known only in a few particular cases. To address some of these outstanding questions, we develop inferential tools for β in a class of factor regression models in which the observed features are signed mixtures of the latent factors. The model specifications are both practically desirable, in a large array of applications, render interpretability to the components of Z, and are sufficient for parameter identifiability. Without assuming that the number of latent factors K or the structure of the mixture is known in advance, we construct computationally efficient estimators of β, along with estimators of other important model parameters. We benchmark the rate of convergence of β by first establishing its ℓ2-norm minimax lower bound, and show that our proposed estimator βˆ is minimax-rate adaptive. Our main contribution is the provision of a unified analysis of the component-wise Gaussian asymptotic distribution of βˆ and, especially, the derivation of a closed form expression of its asymptotic variance, together with consistent variance estimators. The resulting inferential tools can be used when both K and p are independent of the sample size n, and also when both, or either, p and K vary with n, while allowing for p>n. This complements the only asymptotic normality results obtained for a particular case of the model under consideration, in the regime K=O(1) and p→∞, but without a variance estimate. As an application, we provide, within our model specifications, a statistical platform for inference in regression on latent cluster centers, thereby increasing the scope of our theoretical results. We benchmark the newly developed methodology on a recently collected data set for the study of the effectiveness of a new SIV vaccine. Our analysis enables the determination of the top latent antibody-centric mechanisms associated with the vaccine response.

Implications of Correspondence Inference Attribution Theory & Behavioral Jurisprudence Theory: Judge's Conciderations on Conscience

Introduction: This article analyzes the implications of the attribution correspondence inference theory and whether the urge of conscience as affect in the Defendant is the reason for the abolition of the sentence? also behavioral jurisprudence theory on judges' considerations, where it's often a discourse in Indonesia. It is necessary to explore the human values contained in Defendant.Purposes of the Research: The objectives of the research are: First, to provide an analysis of how the theory of attribution correspondence inference to the judge's considerations in determining the circumstances that form the basis of sentencing and knowing whether the urge of conscience as affect in the Defendant is the reason for the abolition of the crime? Second, providing an analysis related to the urgency of behavioral jurisprudence theory in creating fair decisions based on the conscience of the judge.Methods of the Research: The normative legal research method uses a statutory approach, a conceptual approach, and comparisons with different legal system which are based on library research in collecting research legal materials.Results of the Research: The results are: First, the attribution correspondence inference theory can be used as consideration for judges’ considerations in determining to mitigate and aggravating circumstances in Indonesia and with a study of criminal law and the theory that the urge of conscience as affect the defendant can be used as a consideration of mitigating circumstances for the defendant, as well as conditional punishment. Second, the behavioral jurisprudence theory approach has urgency in understanding the behavior of a person doing an act or the judge in making a fair decision.

Inference for low-rank tensors—no need to debias

In this paper, we consider the statistical inference for several low-rank tensor models. Specifically, in the Tucker low-rank tensor PCA or regression model, provided with any estimates achieving some attainable error rate, we develop the data-driven confidence regions for the singular subspace of the parameter tensor based on the asymptotic distribution of an updated estimate by two-iteration alternating minimization. The asymptotic distributions are established under some essential conditions on the signal-to-noise ratio (in PCA model) or sample size (in regression model). If the parameter tensor is further orthogonally decomposable, we develop the methods and nonasymptotic theory for inference on each individual singular vector. For the rank-one tensor PCA model, we establish the asymptotic distribution for general linear forms of principal components and confidence interval for each entry of the parameter tensor. Finally, numerical simulations are presented to corroborate our theoretical discoveries. In all of these models, we observe that different from many matrix/vector settings in existing work, debiasing is not required to establish the asymptotic distribution of estimates or to make statistical inference on low-rank tensors. In fact, due to the widely observed statistical-computational-gap for low-rank tensor estimation, one usually requires stronger conditions than the statistical (or information-theoretic) limit to ensure the computationally feasible estimation is achievable. Surprisingly, such conditions “incidentally” render a feasible low-rank tensor inference without debiasing.

GARCH-type factor model

A new model is proposed for factor analysis of multivariate time series. The latent factors in the model are linked to observed time series through a deterministic relationship in a manner that is similar to the volatility process of the GARCH model. Mathematically-tractable quasi-likelihood is constructed for the proposed GARCH-type factor model, allowing efficient statistical inference even for high-dimensional time series incorporating non-Gaussian idiosyncratic components. Asymptotic theory for statistical inference of the proposed model is also developed. The applicability of the proposed model to real data is demonstrated through macroeconomic data and forward rate data of bonds. The factors extracted are then utilized to elucidate the risk premium of U.S. government bonds.

Task-induced neural covariability as a signature of approximate Bayesian learning and inference.

Perception is often characterized computationally as an inference process in which uncertain or ambiguous sensory inputs are combined with prior expectations. Although behavioral studies have shown that observers can change their prior expectations in the context of a task, robust neural signatures of task-specific priors have been elusive. Here, we analytically derive such signatures under the general assumption that the responses of sensory neurons encode posterior beliefs that combine sensory inputs with task-specific expectations. Specifically, we derive predictions for the task-dependence of correlated neural variability and decision-related signals in sensory neurons. The qualitative aspects of our results are parameter-free and specific to the statistics of each task. The predictions for correlated variability also differ from predictions of classic feedforward models of sensory processing and are therefore a strong test of theories of hierarchical Bayesian inference in the brain. Importantly, we find that Bayesian learning predicts an increase in so-called "differential correlations" as the observer's internal model learns the stimulus distribution, and the observer's behavioral performance improves. This stands in contrast to classic feedforward encoding/decoding models of sensory processing, since such correlations are fundamentally information-limiting. We find support for our predictions in data from existing neurophysiological studies across a variety of tasks and brain areas. Finally, we show in simulation how measurements of sensory neural responses can reveal information about a subject's internal beliefs about the task. Taken together, our results reinterpret task-dependent sources of neural covariability as signatures of Bayesian inference and provide new insights into their cause and their function.

Simultaneous Spatial Panel Data Models with Common Shocks

We consider a simultaneous spatial panel data model, jointly modeling three effects: simultaneous effects, spatial effects and common shock effects. This joint modeling and consideration of cross-sectional heteroscedasticity result in a large number of incidental parameters. We propose two estimation approaches, a quasi-maximum likelihood method and an iterative generalized principal components method. We develop full inferential theories for the estimation approaches and study the tradeoff between the model specifications and their respective asymptotic properties. We further investigate the finite sample performance of both methods using Monte Carlo simulations. We find that both methods perform well and that the simulation results corroborate the inferential theories. Some extensions of the model are considered. Finally, we apply the model to analyze the relationship between trade and gross domestic product using a panel data over time and across countries.

A new robust inference for predictive quantile regression

This paper proposes a novel approach to offer a robust inferential theory across all types of persistent regressors in a predictive quantile regression model. We first estimate a quantile regression with an auxiliary regressor, which is generated as a weighted combination of an exogenous random walk process and a bounded transformation of the original regressor. With a similar spirit of rotation in factor analysis, one can then construct a weighted estimator using the estimated coefficients of the original predictor and the auxiliary regressor. Under some mild conditions, it shows that the self-normalized test statistic based on the weighted estimator converges to a standard normal distribution. Our new approach enjoys a good property that it can reach the local power under the optimal rate T with nonstationary predictor and T for stationary predictor, respectively. More importantly, our approach can be easily used to characterize mixed persistency degrees in multiple regressions. Simulations and empirical studies are provided to demonstrate the effectiveness of the newly proposed approach. The heterogeneous predictability of US stock returns at different quantile levels is reexamined.

Evidence of effectiveness

There are two competing views regarding the role of mechanistic knowledge in inferences about the effectiveness of interventions. One view holds that inferences about the effectiveness of interventions should be based only on data from population-level studies (often statistical evidence from randomised trials). The other view holds that such inferences must be based in part on mechanistic evidence. The competing views are local principles of inference, the plausibility of which can be assessed by a more general normative principle of inference. Bayesianism tells us to base inferences on both the ‘likelihood’ and the ‘prior’. The likelihood represents statistical evidence. One influence on the prior probability of a hypothesis like ‘d causes x’ is mechanistic knowledge of how d causes x. Thus, reasoning about such inferences by appealing to both statistical and mechanistic evidence is vindicated by our best general theory of inference. The primary contribution of this paper is to assess the merits and weaknesses of the arguments on both sides of the debate, using the Bayesian framework. This analysis lends support to those who argue that we should base our causal inferences about interventions in part on mechanistic evidence.

A Condition Monitoring Strategy of Looms Based on DSmT Theory and Genetic Multiobjective Optimization Improves the Rough Set Method

In order to improve the effect of intelligent monitoring and condition analysis of textile machinery, some solutions have been proposed to mitigate incomplete monitoring positions, insufficient decision accuracy, uncertainty reasoning and generalization of the current loom monitoring system. Firstly, a model of the weaving machine spindle dynamics was constructed, and the types and sources of monitoring data were specified. Secondly, an improved rough set method is proposed for processing the collected loom attribute data. A genetic multi-objective optimization method combined with a genetic algorithm is proposed to improve the problem of too many reduction results of the rough set method and improve the monitoring system’s reliability. In order to solve the problem that new objects do not have unique matching rules in the constructed rule base, a fusion of Dezert-Smarandache Theory (DSmT) for uncertainty inference is proposed, which increases the distinguishability of decision support probabilities. Experiments show that the improved rough set method based on DSmT and genetic multi-objective optimization has higher classification accuracy and better recognition than the traditional rough set method for weaving machine condition monitoring.

U POTRAZI ZA „NAUČNIM REVOLUCIJAMA“: SEMIOTIČKI, INFERENCIJALNI I KOGNITIVNO-INFERENCIJALNI MODEL KOMUNIKACIJE

/ The paper examines three dominant models of communication of the previous century – semiotic, inferential and cognitive-inferential model. By presenting and discussing some renowned studies of these models, we trace a certain scientific revolution whereby the dominant beliefs were replaced by a new paradigm. First, we provide a description of Saussure’s achievements within the structuralist approach. Then, we offer a portrayal of the theories that emerged as a reaction to the given model - Grice’s view of communication and the cognitive- inferential relevance theory. Grice introduced a dichotomy of what is said and what is implicated in order to separate the semantic and pragmatic aspects of an utter- ance. Taking into account Grice’s approach, but significantly departing from it, the leading cognitive pragmatists introduced the explicature/implicature distinction, stating that the boundary between the semantic and pragmatic part of an utter- ance is not clear-cut and that the explicit level of communication can also include unarticulated constituents.

A multiplicity of statistical models for modeling the degeneracy of living communicating holons

Communicating living systems detect and process a multiplicity of events with degeneracy, to continuously cope with environmental aleatoric incertitude. The concept of holon, communicating at various scales of living organizations, is hereafter formalized through dynamical systems driven by the multiplicity of statistical models. Then, the stimulus-response of elementary biological holons can be modeled by memoryless Boolean automata with different signal processing methods, in presence of noise and stochastic interference. Detection of a specified signal, to update the automaton state, can be performed via multiple families of update functions, with differentiated balances between sensitivity and specificity in presence of interference: (i) Neyman-Pearson update functions provide the best possible sensitivity to detect the signal of interest in absence of interference, but cannot guarantee a desired specificity; (ii) by detecting large amplitudes of any signal in noise, update functions based on Random Distortion Testing yield a suboptimal sensitivity to detect the signal in noise, but guarantee a wanted specificity even in presence of interference. Thus, statistical inference theories offer functional and structural redundancy and open prospects to model fractal-like holarchies, via networks of communicating degenerated automata, to feature properties of the immune system.

Online self-adaptive proportional-integral-derivative control for brushless DC motor based on variable universe fuzzy inference system optimized by genetic algorithm

In this study, we propose a novel robust online self-adaptive Proportional-Integral-Derivative (PID) control design for Brushless DC Motor (BLDCM) speed system under different operating conditions. The online adaptive tuning for PID parameters is realized accurately by optimizing the control rules of variable universe fuzzy inference with a modified genetic algorithm (GA). Based on the variable fuzzy inference theory, the method of solving contraction–expansion factor in real-time through fuzzy inference is proposed. Furthermore, the process to optimize two inference rules by GA is improved to get optimal control rules for adjusting PID parameters. Finally, multiple sets of simulations and experiments are conducted to validate the proposed controller in different conditions by building Simulink models and setting up experiment platforms. The results of this study not only demonstrate the effectiveness of the proposed controller but also provide technical suggestions for the speed control of BLDCM.

From X to Y: Anatomy of a Constructional Pattern

Compositionality is undoubtedly one of the hardest problems in linguistics. In decoding theories, the speaker occupies a leading role, having to carefully choose the form that better encodes the meaning to be communicated. In contrast, in inferential theories, the burden is shifted from speaker to hearer: linguistic information typically underspecifies meaning and the hearer must make a number of inferences to bridge the gap between what is said and what is meant. In this article, I argue that constructional meaning can aid the process of sentence meaning formation by providing a scaffold that can help the hearer with the construal operations. Constructions, by providing an additional layer of meaning, constrain the range of possible meanings activated by words thereby reducing the combinatorial explosion when several words are joined together. This process is examined here by analysing the meanings associated with the grammatical construction [from X to Y], which is connected to a polysemy network of related senses, using examples extracted from a multimodal corpus. A preliminary analysis of the gesturing behaviour associated with the different senses proposed is also included, which can be seen to contribute to the characterisation of the different senses of the polysemy network.

Estimation and inference in adaptive learning models with slowly decreasing gains

An asymptotic theory for estimation and inference in adaptive learning models with strong mixing regressors and martingale difference innovations is developed. The maintained polynomial gain specification provides a unified framework which permits slow convergence of agents' beliefs and contains recursive least squares as a prominent special case. Reminiscent of the classical literature on co‐integration, an asymptotic equivalence between two approaches to estimation of long‐run equilibrium and short‐run dynamics is established. Notwithstanding potential threats to inference arising from non‐standard convergence rates and a singular variance–covariance matrix, hypotheses about single, as well as joint restrictions remain testable. Monte Carlo evidence confirms the accuracy of the asymptotic theory in finite samples.