Theory Of Inference Research Articles

Prediction of causal genes at GWAS loci with pleiotropic gene regulatory effects using sets of correlated instrumental variables.

Multivariate Mendelian randomization (MVMR) is a statistical technique that uses sets of genetic instruments to estimate the direct causal effects of multiple exposures on an outcome of interest. At genomic loci with pleiotropic gene regulatory effects, that is, loci where the same genetic variants are associated to multiple nearby genes, MVMR can potentially be used to predict candidate causal genes. However, consensus in the field dictates that the genetic instruments in MVMR must be independent (not in linkage disequilibrium), which is usually not possible when considering a group of candidate genes from the same locus. Here we used causal inference theory to show that MVMR with correlated instruments satisfies the instrumental set condition. This is a classical result by Brito and Pearl (2002) for structural equation models that guarantees the identifiability of individual causal effects in situations where multiple exposures collectively, but not individually, separate a set of instrumental variables from an outcome variable. Extensive simulations confirmed the validity and usefulness of these theoretical results. Importantly, the causal effect estimates remained unbiased and their variance small even when instruments are highly correlated, while bias introduced by horizontal pleiotropy or LD matrix sampling error was comparable to standard MR. We applied MVMR with correlated instrumental variable sets at genome-wide significant loci for coronary artery disease (CAD) risk using expression Quantitative Trait Loci (eQTL) data from seven vascular and metabolic tissues in the STARNET study. Our method predicts causal genes at twelve loci, each associated with multiple colocated genes in multiple tissues. We confirm causal roles for PHACTR1 and ADAMTS7 in arterial tissues, among others. However, the extensive degree of regulatory pleiotropy across tissues and the limited number of causal variants in each locus still require that MVMR is run on a tissue-by-tissue basis, and testing all gene-tissue pairs with cis-eQTL associations at a given locus in a single model to predict causal gene-tissue combinations remains infeasible. Our results show that within tissues, MVMR with dependent, as opposed to independent, sets of instrumental variables significantly expands the scope for predicting causal genes in disease risk loci with pleiotropic regulatory effects. However, considering risk loci with regulatory pleiotropy that also spans across tissues remains an unsolved problem.

Active Inference Models of AV Takeovers: Relating Model Parameters to Trust, Situation Awareness, and Fatigue.

Our objectives were to assess the efficacy of active inference models for capturing driver takeovers from automated vehicles and to evaluate the links between model parameters and self-reported cognitive fatigue, trust, and situation awareness. Control transitions between human drivers and automation pose a substantial safety and performance risk. Models of driver behavior that predict these transitions from data are a critical tool for designing safer, human-centered, systems but current models do not sufficiently account for human factors. Active inference theory is a promising approach to integrate human factors because of its grounding in cognition and translation to a quantitative modeling framework. We used data from a driving simulation to develop an active inference model of takeover performance. After validating the model's predictions, we used Bayesian regression with a spike and slab prior to assess substantial correlations between model parameters and self-reported trust, situation awareness, fatigue, and demographic factors. The model accurately captured driving takeover times. The regression results showed that increases in cognitive fatigue were associated with increased uncertainty about the need to takeover, attributable to mapping observations to environmental states. Higher situation awareness was correlated with a more precise understanding of the environment and state transitions. Higher trust was associated with increased variance in environmental conditions associated with environmental states. The results align with prior theory on trust and active inference and provide a critical connection between complex driver states and interpretable model parameters. The active inference framework can be used in the testing and validation of automated vehicle technology to calibrate design parameters to ensure safety.

The Gods as Latent Causes: A Statistical Inference Theory of Religion

ABSTRACT The statistical brain hypothesis posits that the brain constructs probabilistic models of the environment. Here we examine whether this perspective can provide any insight on religion. We propose that religious ideas represent an attempt to explain away residuals, that is, to explain discrepancies between observations and predictions. The framework postulates probabilistic generative models where gods are described by latent variables whose possible states correspond to the actions available to the gods. As examined in the paper, our proposal offers a plausible interpretation of typical religious phenomena such as miracles, omens, and divination. Moreover, it captures important characteristics of religious beliefs including the notion that gods control multiple spheres of reality, are organized hierarchically, and control aspects that are salient for believers. Besides offering an intriguing new perspective on religion, the paper corroborates the possibility that the statistical brain hypothesis represents a unifying theory of the mind.

Care-theoretic semantics

Abstract This paper argues for two claims, one conservative and the other one less so. First is the thesis that the range of linguistic features that can be considered authentically semantic is far wider in breadth than is typically acknowledged. In particular, linguistic phenomena that are entirely orthogonal to veridical considerations—including ethical matters—are sufficiently robust to support e.g. theories of meaning and inference. Second, we aim to argue that philosophy’s prioritization of truth obscures the existence of an important prior concern for care or the reduction of harm. Specifically, we offer a topic-theoretic interpretation of the bounds consequence reading of sequents put forward by Restall and Ripley. We argue that this reading allows us to provide a rigorous semantics using the newly introduced categories of care and harm. An important objection may be that philosophy requires the centrality and priority of truth above all else insofar as truth-like features are necessary to ground theories of meaning or reasoning. Anticipating such an objection, we will argue that care not only is an authentically semantic feature but also that care is sufficient to ground such theories.

Meanings of Basqueness: An Account from Brandomian Inferentialism on Basque Identity and Its Evolution

ABSTRACT In this paper, I will analyze how the conceptualization of the term ‘Basqueness’, and the meaning of ‘being Basque’, have been instrumental in determining the way in which citizens of the Basque Autonomous Community (BAC) have chosen to identify themselves. What it means to be Basque has experienced a significant evolution from its ethnic and linguistic origins to being a matter of feeling and doing; however, the way Basque citizens felt about Basqueness, and what actions participated in Basqueness, was substantially influenced by the climate of political polarization, the appropriation by nationalism of Basque symbols and features and the violence of terrorist organization ETA, as well as the justification of its crimes. Working from the semantic theory of Brandomian inferentialism, I want to show how these factors functioned as inferential relations that constituted the meaning of Basqueness. As these inferences changed with the dissolution of ETA and the easing of political polarization, what it means to be Basque has subsequently changed its meaning, which might contribute to explaining why in recent years an increasing number of people in the BAC have incorporated Basqueness into their mixed or liminal identities.

Deep learning coupled Bayesian inference method for measuring the elastoplastic properties of SS400 steel welds by nanoindentation experiment

Nanoindentation experiment has shown broad application prospects due to its ability to measure the mechanical properties of various materials at multiple scales. In this paper, a deep learning coupled Bayesian inverse approach is proposed for measuring the elastoplastic parameters of SS400 steel welds by nanoindentation experiment. The nanoindentation experiments were performed on the SS400 steel welds, including base metal (BM), weld zone (WZ), and heat affected zone (HAZ), and the experiment load–displacement (P-h) curves were obtained. The hyper-parameters tunable artificial neural network (ANN) was established to correlate elastoplastic parameters with indentation P-h curves. Based on Bayesian inference theory, the posterior density function for estimating the unknown material parameters was established. Transitional Markov chain Monte Carlo was used for sampling from the posterior density function, and the elastoplastic properties in different regions of SS400 steel welds were identified. The advantage of the established measuring method is that the hyper-parameters optimized ANN model can provide the very accurate forward relationship between material properties and indentation P-h curves. Besides, the inverse Bayesian framework can quantify the potential uncertainty of the identified elastoplastic parameters. The measured elastoplastic properties of the base metal of SS400 steel show good agreement with tensile experiment data, of which the maximum measuring error is less than 12%. The measured elastoplastic properties in WZ and HAZ are also proved to be effective. The uncertainty of the identified elastoplastic parameters of SS400 steel welds can be quantified by posterior marginal distribution, using Mean and Variance values. The results proved that the proposed inverse measuring method is reliable and effective.

Inference on the eigenvalues of the normalized precision matrix

Recent developments in the spectral theory of Bayesian Networks has led to a need for a developed theory of estimation and inference on the eigenvalues of the normalized precision matrix, Ω. In this paper, working under conditions where n→∞ and p remains fixed, we provide multivariate normal asymptotic distributions of the sample eigenvalues of Ω under general conditions and under normal populations, a formula for second-order bias correction of these sample eigenvalues, and a Stein-type shrinkage estimator of the eigenvalues. Numerical simulations are performed which demonstrate under what generative conditions each estimation technique is most effective. When the largest eigenvalue of Ω is small the simulations show that the second order bias-corrected eigenvalue was considerably less biased than the sample eigenvalue, whereas the smallest eigenvalue was estimated with less bias using either the sample eigenvalue or the proposed shrinkage method.

Feasibility of a Personal Neuromorphic Emulation.

The representation of intelligence is achieved by patterns of connections among neurons in brains and machines. Brains grow continuously, such that their patterns of connections develop through activity-dependent specification, with the continuing ontogenesis of individual experience. The theory of active inference proposes that the developmental organization of sentient systems reflects general processes of informatic self-evidencing, through the minimization of free energy. We interpret this theory to imply that the mind may be described in information terms that are not dependent on a specific physical substrate. At a certain level of complexity, self-evidencing of living (self-organizing) information systems becomes hierarchical and reentrant, such that effective consciousness emerges as the consequence of a good regulator. We propose that these principles imply that an adequate reconstruction of the computational dynamics of an individual human brain/mind is possible with sufficient neuromorphic computational emulation.

Self correction technique for amplitude and phase errors of underwater acoustic arrays based on variable decibel Bayesian inference

Abstract Direction of arrival (DOA) estimation of underwater targets is an important research direction in the field of underwater acoustic array signal processing. Due to factors such as assembly errors and component aging, amplitude and phase errors between each channel of the array are common, resulting in distortion of the array flow pattern matrix. At the same time, the non-uniform distribution of noise intensity at the positions of each array element further increases the difficulty of DOA for underwater targets. Based on the characteristics of sparse spatial distribution of underwater targets, this paper uses the flexibility of hyperparametric modeling to construct an array observation signal graph model. On this basis, the effects of amplitude and phase errors and non-uniform noise on the array model are added, and a comprehensive observation model with clear distribution types of amplitude and phase errors, signals, and noise is established. The variational Bayesian inference theory is used to obtain closed solutions of various variables and parameters, so as to achieve joint estimation of target azimuth and error parameters. Simulation results and sea trial data processing results show that the algorithm can achieve amplitude and phase error self correction while completing DOA estimation in the absence of prior information on the number of sources.

Bootstrap inference on a factor model based average treatment effects estimator

We propose a novel bootstrap procedure for conducting inference for factor model-based average treatment effects estimators. Our method overcomes bias inherent to existing bootstrap procedures and substantially improves upon existing large sample normal inference theory in small sample settings. The finite sample improvements arising from the use of our proposed procedure are illustrated via a set of Monte Carlo simulations, and formal justification for the procedure is outlined.

Axiomatization of interventional probability distributions

Abstract Causal intervention is an essential tool in causal inference. It is axiomatized under the rules of do-calculus in the case of structure causal models. We provide simple axiomatizations for families of probability distributions to be different types of interventional distributions. Our axiomatizations neatly lead to a simple and clear theory of causality that has several advantages: it does not need to make use of any modelling assumptions such as those imposed by structural causal models; it only relies on interventions on single variables; it includes most cases with latent variables and causal cycles; and more importantly, it does not assume the existence of an underlying true causal graph as we do not take it as the primitive object; moreover, a causal graph is derived as a by-product of our theory. We show that, under our axiomatizations, the intervened distributions are Markovian to the defined intervened causal graphs, and an observed joint probability distribution is Markovian to the obtained causal graph; these results are consistent with the case of structural causal models, and as a result, the existing theory of causal inference applies. We also show that a large class of natural structural causal models satisfy the theory presented here. The aim of this paper is axiomatization of interventional families, which is subtly different from causal modelling.

Inferring general links between energetics and information with unknown environment

Identifying general links between energetics and information in realistic open quantum systems is a long-standing problem in quantum thermodynamics and quantum information processing. However, the generality of existing efforts is often impeded by their specific assumptions about environments. Here we address the problem by developing a trajectory-level thermodynamic inference theory to establish general links using just the knowledge of the system, enabling a single framework applicable to a diverse range of environments. Underpinning the framework is a notion of excess energy introduced for inferring the net energy gain of the system after completing an information processing trajectory. We show that fluctuation behaviors of the excess energy encode general links between energetics and information with a conceptual advantage that they completely avoid assumptions about environments and system-environment coupling forms. Crucially, we obtain a single thermodynamic inequality that integrates upper bounds on heat dissipation and extracted work in terms of system's information content change, providing complementary constraints that greatly expand the context of existing well-adopted results based on the second law of thermodynamics. We also uncover lower bounds on the precision of the fluctuating system's energy and information content changes in terms of their Fano factors and a correlation function between them. By extending relations between energetics and information to higher-order fluctuations, we thus reveal a trade-off that a more precise inference of energy or information content changes requires a looser energetic-information link. We showcase the implications of these general links in a number of quantum information and thermodynamic tasks of application relevance. Our framework provides a toolkit for analyzing the interplay between energetics and information from the trajectory level in generic quantum systems, thereby adding an indispensable structure to the thermodynamics of information. Published by the American Physical Society 2024

Threshold spatial autoregressive model

In this paper, we consider the estimation and inferential issues of the threshold spatial autoregressive (TSAR) model, which is a hybrid of the threshold and spatial autoregressive models. We use the quasi maximum likelihood (QML) method to estimate the model. In addition, we prove the tightness and the Hájek–Rényi type inequality for a quadratic form and establish a full inferential theory of the QML estimator under the setup that threshold effect shrinks to zero as the sample size increases. We conduct hypothesis testing on the presence of the threshold effect, using three super-type statistics. Their asymptotic behaviors are studied under the Pitman local alternatives. A bootstrap procedure is applied to obtain the asymptotically correct critical value. We also consider hypothesis testing on the threshold value set equal to a prespecified one. We run Monte Carlo simulations to investigate the finite sample performance of the QML estimators and find that the estimators perform well. In an empirical application, we apply the proposed TSAR model to study the relationship between financial development and economic growth, and we find firm evidence to support the TSAR model.

Aspects of a logical theory of assertion and inference

AbstractThe aim here is to investigate assertion and inference as notions of logic. Assertion will be explained in terms of its purpose, which is to give interlocutors the right to request the assertor to do a certain task. The assertion is correct if, and only if, the assertor knows how to do this task. Inference will be explained as an assertion equipped with what I shall call a justification profile, a strategy for making good on the assertion. The inference is valid if, and only if, correctness is preserved from the premiss assertions to the conclusion assertion. Most of this is a spelling out of views on assertion and inference presented by Per Martin‐Löf in lectures since 2015.

A practical real-time control strategy for high-performance building HVAC operation concerning potential pandemic outbreaks in post-pandemic era

The lessons learned from the COVID-19 pandemic emphasize the need for building HVAC systems to be adaptable and prepared to adjust their operation for infection risk mitigation for future pandemics. Most guidelines suggest increasing outdoor air fraction in HVAC systems during pandemics. However, this approach is not energy-efficient due to the conditioning of additional outdoor air, and many practical systems face limitations in introducing more outdoor air than normal due to capacity or design constraints. Therefore, there is an urgent need for an alternative solution for HVAC system operation during pandemics that is energy-efficient, can effectively mitigate infection risks and does not require extensive retrofits to existing systems. To meet this need, this study proposes a practical real-time control strategy for high-performance HVAC operation in response to potential pandemic outbreaks. First, a supply air temperature (SAT) reset scheme for infection risk mitigation is proposed based on Mamdani fuzzy inference theory. Then, a coordinated control scheme is proposed to coordinate SAT reset with chilled water temperature adjustment for energy-efficiency enhancement. This strategy does not require system equipment retrofits but only changes in control logic to transition to pandemic operation. The effectiveness of the proposed strategy is validated in a high-fidelity Modelica-based simulation environment under various weather conditions. Results show that, compared to normal operation, the proposed strategy can significantly reduce new infection cases by 32∼46 % without increasing energy use, and may even achieve slight energy savings of 9∼13 %, while maintaining expected occupant comfort.

Inferential theories of retrospective confidence

Inferential theories of retrospective confidence

The Logical Perspective in Pragma-dialectics

AbstractI argue that the logical perspective—the study of arguments as products—is not well integrated into pragma-dialectics. I show that the Validity Rule and the Argumentation Scheme Rule, despite being procedural rules, are, in a certain sense, “logical” rules. Subsequently, I distinguish and review three successive periods in the development of the logical dimension of pragma-dialectics: conventionalist, inferentialist and dualist, to reveal that none of them is completely satisfactory. I contend that, given the assumptions and conceptual apparatus of pragma-dialectics, the integration of the logical perspective, and especially of a suitable account of counterargumentation, requires the adoption of a conception of logic as a theory of reasons, as opposed to the traditional conception of logic as a theory of inferences. Understanding logic as a theory of the dialogical construction of reasons enables us to approach the study of the relationships between arguments and the weighing of opposing arguments.

Sensory marketing: the mediating role of hedonic emotions between multisensory experiences and mall patronage intention

PurposeProviding positive and memorable shopping experiences has become essential for retailers. As the retail industry strives to create multisensory experiences for consumers, it is equally important to understand the emotions and pleasure such experiences evoke. The current study aims to investigate how multisensory experiences induce hedonic emotions in retail shoppers. It explores the mediating role of hedonic emotions in between multisensory experiences and shopping mall patronage intention.Design/methodology/approachThe study is descriptive; the data was collected using the mall intercept survey method. A total of 380 shoppers participated in and responded to the survey administered at the mall. The collected responses are analyzed using partial least squares structural equation modeling.FindingsOur findings support the idea that hedonic emotions play a significant role in the retail environment by influencing retail patronage intentions. Also, the multisensory experiences have a positive effect on patronage intention as well as the hedonic emotions of shoppers.Research limitations/implicationsThe present study provides theoretical and managerial implications for academicians and retail marketing.Originality/valueThe present research contributes to the existing literature by introducing the concept of multisensory experiences in the retail context and its impact on hedonic emotions and adding to the concept of inference theory.

Smoke and Fire. Sign Inference in Greek and in Indian Epistemology

Abstract“Wherever there is smoke there must be fire.” In 1957, Aram M. Frenkian noticed that both ancient Greek and Indian philosophy makes use of the smoke-fire analogy as a model for inferential reasoning. He postulated that Greek use of the example reflected Indian influence on Greek philosophy which was mediated through the works of Pyrrho, the founder of Sceptisicm, who had accompanied Alexander the Great on his Indian campaign (327-5 BCE) and learnt from Indian sages (‘gymnosophists’ as the Greeks called them). More recent scholarship mainly shares his views. In the present paper, I argue that the smoke-fire example formed part of a complex theory of sign inference, and that not only the isolated analogy, but the theory as a whole which it illustrates is present in both cultures. Furthermore, the variants of this theory comprise such close and extended similarities that they must be the result of direct contact between Indian and Greek thinkers, although it is impossible to decide whether they stem from a common origin, or are the result of influence from one culture to the other.

An Unfortunate Refutation: Ibn Taymīyah on the Priority of Reason over Revelation

Ibn Taymīyah singled out his long refutation against an Ash‘arite theologian Fakhr al-Dīn al-Rāzī on ta’wīl in his Dar’ Ta‘āruḍ al-‘Aql wa al-Naql. The main contention between the two is whether rational evidence should take precedence over scriptural evidence when interpreting passages in the Qur’an. When dealing with the difficult passages that could lead to anthropomorphism, the Ash‘arite employ allegorical interpretation, or ta’wīl, thereby priority is given to reason. It is with this problem in mind that Ibn Taymīyah launched his sustained critique towards al-Rāzī. However, upon further consideration, Ibn Taymīyah’s criticism of al-Rāzī appears to overlook the broader framework that the latter brought to the discussion, particularly regarding the reciprocal relationship between reason and revelation in his theory of inference and its origins in earlier Asharīte’s epistemology. This article aims to discuss the debates between the two scholars by putting their main contention in the context. Firstly, the article will provide an analysis on al-Rāzī’s methodological and epistemological approach to resolving the problem of the relationship between reason and revelation. The folllowing section will exemine how Ibn Taymīyah responded to al-Rāzī, and the criticism he leveled against him. The final section will assess the debates, evaluating the extent to which Ibn Taymīyah succeeded in refuting al-Rāzī and identifying any misunderstandings he may have had regarding al-Rāzī’s arguments.