Correct Inference Research Articles

Superior probabilistic computing using operationally stable probabilistic-bit constructed by manganite nanowire

Abstract Probabilistic computing has emerged as a viable approach to treat optimization problems. To achieve superior computing performance, the key aspect during computation is massive sampling and tuning on the probability states of each probabilistic bit (p-bit), demanding its high stability under extensive operations. Here, we demonstrate a p-bit constructed by manganite nanowire that shows exceptionally high stability. The p-bit contains an electronic domain that fluctuates between metallic (low resistance) and insulating (high resistance) states near its transition temperature. The probability for the two states can be directly controlled by nano-ampere electrical current. Under extensive operations, the standard error of its probability values is less than 1.3%. Simulations show that our operationally stable p-bit plays the key role to achieve correct inference in Bayesian network by strongly suppressing the relative error, displaying the potential for superior computing performance. Our p-bit also serves as high quality random number generator without extra data-processing, beneficial for cryptographic applications.

Nonstationary A/B Tests: Optimal Variance Reduction, Bias Correction, and Valid Inference

We develop an analytical framework to appropriately model and adequately analyze A/B tests in presence of nonparametric nonstationarities in the targeted business metrics. A/B tests, also known as online randomized controlled experiments, have been used at scale by data-driven enterprises to guide decisions and test innovative ideas to improve core business metrics. Meanwhile, nonstationarities, such as the time-of-day effect and the day-of-week effect, can often arise nonparametrically in key business metrics involving purchases, revenue, conversions, customer experiences, and so on. First, we develop a generic nonparametric stochastic model to capture nonstationarities in A/B test experiments, where each sample represents a visit or action associated with a time label. We build a practically relevant limiting regime to facilitate analyzing large-sample estimator performances under nonparametric nonstationarities. Second, we show that ignoring or inadequately addressing nonstationarities can cause standard A/B test estimators to have suboptimal variance and nonvanishing bias, therefore leading to loss of statistical efficiency and accuracy. We provide a new estimator that views time as a continuous strata and performs poststratification with a data-dependent number of stratification levels. Without making parametric assumptions, we prove a central limit theorem for the proposed estimator and show that the estimator attains the best achievable asymptotic variance and is asymptotically unbiased. Third, we propose a time-grouped randomization that is designed to balance treatment and control assignments at granular time scales. We show that when the time-grouped randomization is integrated to standard experimental designs to generate experiment data, simple A/B test estimators can achieve asymptotically optimal variance. A brief account of numerical experiments are conducted to illustrate the analysis. This paper was accepted by Baris Ata, stochastic models and simulation. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.01205 .

A novel model for count data: zero-inflated Probit Bell model with applications

A novel model for count data is proposed in this work. This new model is briefly called the Zero-Inflated Probit Bell (ZIPBell) model. The ZIPBell model can be used to simulate and analyze traditional count data and can be used well in the simulation and analysis of count data with the phenomenon that the frequency for zero occupies a very high percentage. In the scope of this study, we first provide the general formula, functions or related equations for the ZIPBell model. The results of asymptotic inferences for the ZIPBell model are also given in this work. Maximum likelihood estimator (MLE) is used to check its performance of the proposed model. According to the numerical illustrations via several simulation researches and the analysis of a practical data set, it has been seen that the MLE method has offered correct and reliable inferences. Finally, the inferences and conclusions of this study are mentioned.

Impact of Risk Material Misstatements Assessment on Indonesia Audit Practices

Objective: The purpose of this study is to determine what factors could influence the determination of the auditor's Risk Material Misstatement (RMM) in Indonesia. Theoretical Framework: Auditors need to perform a variety of tasks to form an overall assurance or attestation opinion. To do so, various personal attributes of the auditor influence the outcome. In the process of applying these personal attributes towards the performance of the task, auditors interact with other auditors and stakeholders of the firm. Method: Sources of data come from interviews with several auditors in Indonesia and regulators namely IAPI and the Financial Services Authority (OJK). Results and Discussion: The results showed that the factors affect the level of RMM are due to government regulatory factors, individual factors and cultural factors. Research Implications: Government regulatory factors that can influence auditors in determining RMM: 1) encourage an increase in personnel capabilities, 2) increase in skill abilities, and 3) self-awareness of the ability to carry out duties and responsibilities. Then the individual factors that can influence the determination of the Auditor's RMM include: 1) understanding of culture and governance, 2) determination of risk, 3) potential and human resources owned by the auditor. On the other hand, cultural factors in the field of auditing that influence auditors are less critical or profound, contributing factors that do not genuinely accept the distribution. Originality/Value: This paper focuses specifically on the auditor’s cognitive performance – the auditor’s ability to make normatively correct inferences about testing hypotheses in the course of the audit.

Reasoning about possibilities: Modal logics, possible worlds, and mental models.

Everyone reasons about possibilities. This article explains how they could do so using mental models. The theory makes four major claims: 1. Correct inferences are necessary, referring only to facts or possibilities to which the premises refer and not ruling any of them out, for example: She left or hid; Therefore, it's possible that she left and possible that she hid. 2. A possibility such as that she hid, which is represented in an intuitive model, presupposes the possibility that it did not occur, she did not hide, which, if reasoners deliberate, is represented in the resulting model. 3. Reasoners condense consistent possibilities, such as the earlier pair, into one possibility: it is possible that she left and she hid. 4. Inconsistencies, such as she left or hid, and she neither left nor hid, refer to no possibilities whatsoever - they have an empty model - and so their only effects are local. Hence, any inference can be withdrawn with impunity if there is knowledge to the contrary. Experiments have corroborated each of these principles. They are incompatible with four essentials of standard modal logics, which concern deductions based on "possible" or "necessary". Their formal deductions correspond to valid inferences, which have no counterexamples in which the premises are true but the conclusion is false. And so the article examines the differences between the two approaches, and explores the adaptation of a modal logic to account for correct human reasoning. Its feasibility is an open question.

Language models, like humans, show content effects on reasoning tasks.

reasoning is a key ability for an intelligent system. Large language models (LMs) achieve above-chance performance on abstract reasoning tasks but exhibit many imperfections. However, human abstract reasoning is also imperfect. Human reasoning is affected by our real-world knowledge and beliefs, and shows notable "content effects"; humans reason more reliably when the semantic content of a problem supports the correct logical inferences. These content-entangled reasoning patterns are central to debates about the fundamental nature of human intelligence. Here, we investigate whether language models-whose prior expectations capture some aspects of human knowledge-similarly mix content into their answers to logic problems. We explored this question across three logical reasoning tasks: natural language inference, judging the logical validity of syllogisms, and the Wason selection task. We evaluate state of the art LMs, as well as humans, and find that the LMs reflect many of the same qualitative human patterns on these tasks-like humans, models answer more accurately when the semantic content of a task supports the logical inferences. These parallels are reflected in accuracy patterns, and in some lower-level features like the relationship between LM confidence over possible answers and human response times. However, in some cases the humans and models behave differently-particularly on the Wason task, where humans perform much worse than large models, and exhibit a distinct error pattern. Our findings have implications for understanding possible contributors to these human cognitive effects, as well as the factors that influence language model performance.

Study on the Domain Adaption of Korean Speech Act using Daily Conversation Dataset and Petition Corpus

In Korean, quantitative speech act studies have usually been conducted on single utterances with unspecified sources. In this study, we annotate sentences from the National Institute of Korean Language's Messenger Corpus and the National Petition Corpus, as well as example sentences from an academic paper on contemporary Korean vlogging, and check the discrepancy between human annotation and model prediction. In particular, for sentences with differences in locutionary and illocutionary forces, we analyze the causes of errors to see if stylistic features used in a particular domain affect the correct inference of speech act. Through this, we see the necessity to build and analyze a balanced corpus in various text domains, taking into account cases with different usage roles, e.g., messenger conversations belonging to private conversations and petition corpus/vlogging script that have an unspecified audience.

On the Dynamics of Inferential Behavior while Reading Expository and Narrative Texts.

Inference plays a key role in reading comprehension. This study examines changes in inferential behavior while reading different genres. The inferential behavior of 28 students with reading disabilities (RDs) and 44 students without RDs was quantified while they read expository and narrative texts. First, the average rates of inference attempts and correct inferences were measured during reading. Then, the same rates were measured separately during early and late reading to see if there was a change in inferential behavior. The results show that the change in inferential behavior depends on the genre. While reading the expository text, both groups showed no significant change in their inference making. In contrast, while reading the narrative text, both groups showed higher rates of inference attempts, and only the students without RD showed a significant increase in correct inferences. The implications of these findings for the design of more engaging and effective reading programs are discussed.

On variance estimation of the inverse probability-of-treatment weighting estimator: A tutorial for different types of propensity score weights.

Propensity score methods, such as inverse probability-of-treatment weighting (IPTW), have been increasingly used for covariate balancing in both observational studies and randomized trials, allowing the control of both systematic and chance imbalances. Approaches using IPTW are based on two steps: (i) estimation of the individual propensity scores (PS), and (ii) estimation of the treatment effect by applying PS weights. Thus, a variance estimator that accounts for both steps is crucial for correct inference. Using a variance estimator which ignores the first step leads to overestimated variance when the estimand is the average treatment effect (ATE), and to under or overestimated estimates when targeting the average treatment effect on the treated (ATT). In this article, we emphasize the importance of using an IPTW variance estimator that correctly considers the uncertainty in PS estimation. We present a comprehensive tutorial to obtain unbiased variance estimates, by proposing and applying a unifying formula for different types of PS weights (ATE, ATT, matching and overlap weights). This can be derived either via the linearization approach or M-estimation. Extensive R code is provided along with the corresponding large-sample theory. We perform simulation studies to illustrate the behavior of the estimators under different treatment and outcome prevalences and demonstrate appropriate behavior of the analytical variance estimator. We also use a reproducible analysis of observational lung cancer data as an illustrative example, estimating the effect of receiving a PET-CT scan on the receipt of surgery.

Use and reporting of inverse-probability-of-treatment weighting for multicategory treatments in medical research: a systematic review

Causal inference methods for observational data represent an alternative to randomised controlled trials when they are not feasible or when real-world evidence is sought. Inverse-probability-of-treatment weighting (IPTW) is one of the most popular approaches to account for confounding in observational studies. In medical research, IPTW is mainly applied to estimate the causal effect of a binary treatment, even when the treatment has in fact multiple categories, despite the availability of IPTW estimators for multiple treatment categories. This raises questions about the appropriateness of the use of IPTW in this context. Therefore, we conducted a systematic review of medical publications reporting the use of IPTW in the presence of a multi-category treatment. Our objectives were to investigate the frequency of use and the implementation of these methods in practice, and to assess the quality of their reporting. Using Pubmed, Embase and Web of Science, we screened 5660 articles and retained 106 articles in the final analysis that were from 17 different medical areas. This systematic review is registered on PROSPERO (CRD42022352669). The number of treatment groups varied between 3 and 9, with a large majority of articles (90 [84.9%]) including 3 or 4 groups. The most commonly used method for estimating the weights was multinomial regression (51 [48.1%]) and generalized boosted models (48 [45.3%]). The covariates of the weight model were reported in 91 articles (85.9 %). Twenty-six articles (24.5 %) did not discuss the balance of covariates after weighting, and only 16 articles (15.1 %) referred to the assumptions needed to obtain correct inferences. The results of this systematic review illustrate that medical publications scarcely use IPTW methods for more than two treatment categories. Among the publications that did, the quality of reporting was suboptimal, in particular in regard to the assumptions and model building. IPTW for multi-category treatments could be applied more broadly in medical research, and the application of the proposed guidelines in this context will help researchers to report their results and to ensure reproducibility of their research.

Human-robot facial coexpression.

Large language models are enabling rapid progress in robotic verbal communication, but nonverbal communication is not keeping pace. Physical humanoid robots struggle to express and communicate using facial movement, relying primarily on voice. The challenge is twofold: First, the actuation of an expressively versatile robotic face is mechanically challenging. A second challenge is knowing what expression to generate so that the robot appears natural, timely, and genuine. Here, we propose that both barriers can be alleviated by training a robot to anticipate future facial expressions and execute them simultaneously with a human. Whereas delayed facial mimicry looks disingenuous, facial coexpression feels more genuine because it requires correct inference of the human's emotional state for timely execution. We found that a robot can learn to predict a forthcoming smile about 839 milliseconds before the human smiles and, using a learned inverse kinematic facial self-model, coexpress the smile simultaneously with the human. We demonstrated this ability using a robot face comprising 26 degrees of freedom. We believe that the ability to coexpress simultaneous facial expressions could improve human-robot interaction.

Bayesian Inference for Repeated Measures Under Informative Sampling

Survey data are often randomly drawn from an underlying population of inferential interest under a multistage, complex sampling design. A sampling weight proportional to the number of individuals in the population that each sampled individual represents is released. The sampling design is informative with respect to a response variable of interest if the variable correlates with the sampling weights. The distribution for the variables of interest differs in the sample and in the population, requiring correction to the sample distribution to approximate the population. We focus on model-based Bayesian inference for repeated (continuous) measures associated with each sampled individual. We devise a model for the joint estimation of response variable(s) of interest and sampling weights to account for the informative sampling design in a formulation that captures the association of the measures taken on the same individual incorporating individual-specific random-effects. We show that our approach yields correct population inference on the observed sample of units and compare its performance with competing method via simulation. Methods are compared using bias, mean square error, coverage, and length of credible intervals. We demonstrate our approach using a National Health and Nutrition Examination Survey dietary dataset modeling daily protein consumption.

That's not the Mona Lisa! How to interpret spatial capture-recapture density surface estimates.

Spatial capture-recapture methods are often used to produce density surfaces, and these surfaces are often misinterpreted. In particular, spatial change in density is confused with spatial change in uncertainty about density. We illustrate correct and incorrect inference visually by treating a grayscale image of the Mona Lisa as an activity center intensity or density surface and simulating spatial capture-recapture survey data from it. Inferences can be drawn about the intensity of the point process generating activity centers, and about the likely locations of activity centers associated with the capture histories obtained from a single survey of a single realization of this process. We show that treating probabilistic predictions of activity center locations as estimates of the intensity of the process results in invalid and misleading ecological inferences, and that predictions are highly dependent on where the detectors are placed and how much survey effort is used. Estimates of the activity center density surface should be obtained by estimating the intensity of a point process model for activity centers. Practitioners should state explicitly whether they are estimating the intensity or making predictions of activity center location, and predictions of activity center locations should not be confused with estimates of the intensity.

Men with high dark triad personality traits can accurately infer dark triad traits from other people's faces.

The literature suggests that people can accurately infer dark triad (DT) personality traits from other peoples' faces. Using a self-report scale, this study investigated the impact of participants' DT personality traits on their ability to accurately infer other peoples' DT traits from facial cues. We created composite facial photographs of Japanese people with varying Machiavellianism, psychopathy, and narcissism scores. The Japanese participants (N = 170) assessed these three DT traits in the facial photographs and completed a questionnaire that assessed their own DT traits. The results indicated that the participants could accurately infer all three DT traits from female faces but not from male faces. Male participants showed a positive correlation between accurate inferences of other men's Machiavellianism and their own DT traits. In contrast, female participants showed a negative correlation between correct inferences of men's DT traits and their own DT traits. These findings offer novel insights into human evolutionary and social adaptations.

The Life Stories of Teacher Leaders: A Qualitative Content Analysis

The teacher leader creates a path through which the students achieve their valued outcomes (high education performance). The qualitative content analysis was used in the investigation. The application of correct inference and interpretation in qualitative content analysis allows for the raw data into manageable themes. 10 participants were recruited in the study. Each interview conducted in their own office in the school. Semi-structured interviews were done based on the general questions asked with the key informants. This set of interview guides were evaluated twice by two experts in qualitative research. All interviews were recorded and then transcribed by the researcher. Seven themes emerged in this study: questing for quality education through continuous involvement in curriculum development, nurturing an empowering and inspiring school climate, leveraging partnerships, networks and linkages with the various stakeholders, exemplifying good leadership skills, amplifying open and clear communication, developing systematic process of classroom observation and setting attainable standards outcomes and expectations for students and teachers. This study revealed the actions of teacher leaders working at Quirino National High School about teacher leadership skills and their best practices. The seven themes show that teacher leaders are highly motivated to enhance student achievement. Stakeholders in education should place significant emphasis on promoting classroom teacher leadership. Thus, education administrators should design effective strategies to ensure that the current crop of teachers understands the importance and role of teacher leaders. By reflecting on these practices, teachers will be inspired to become teacher leaders in their own field.

J-score: a robust measure of clustering accuracy.

Clustering analysis discovers hidden structures in a data set by partitioning them into disjoint clusters. Robust accuracy measures that evaluate the goodness of clustering results are critical for algorithm development and model diagnosis. Common problems of clustering accuracy measures include overlooking unmatched clusters, biases towards excessive clusters, unstable baselines, and difficulties of interpretation. In this study, we presented a novel accuracy measure, J-score, to address these issues. Given a data set with known class labels, J-score quantifies how well the hypothetical clusters produced by clustering analysis recover the true classes. It starts with bidirectional set matching to identify the correspondence between true classes and hypothetical clusters based on Jaccard index. It then computes two weighted sums of Jaccard indices measuring the reconciliation from classes to clusters and vice versa. The final J-score is the harmonic mean of the two weighted sums. Through simulation studies and analyses of real data sets, we evaluated the performance of J-score and compared with existing measures. Our results show that J-score is effective in distinguishing partition structures that differ only by unmatched clusters, rewarding correct inference of class numbers, addressing biases towards excessive clusters, and having a relatively stable baseline. The simplicity of its calculation makes the interpretation straightforward. It is a valuable tool complementary to other accuracy measures. We released an R/jScore package implementing the algorithm.

Short‐term forecasting with a computationally efficient nonparametric transfer function model

SummaryIn this paper a semi‐parametric approach is developed to model non‐linear relationships in time series data using polynomial splines. Polynomial splines require very little assumption about the functional form of the underlying relationship, so they are very flexible and can be used to model highly non‐linear relationships. Polynomial splines are also computationally very efficient. The serial correlation in the data is accounted for by modelling the noise as an autoregressive integrated moving average (ARIMA) process, by doing so, the efficiency in nonparametric estimation is improved and correct inferences can be obtained. The explicit structure of the ARIMA model allows the correlation information to be used to improve forecasting performance. An algorithm is developed to automatically select and estimate the polynomial spline model and the ARIMA model through backfitting. This method is applied on a real‐life data set to forecast hourly electricity usage. The non‐linear effect of temperature on hourly electricity usage is allowed to be different at different hours of the day and days of the week. The forecasting performance of the developed method is evaluated in post‐sample forecasting and compared with several well‐accepted models. The results show the performance of the proposed model is comparable with a long short‐term memory deep learning model.

فعالية تعليم المناظرة العلمية لترقية مهارة الكلام

Debate is one of the linguistic skills from the branch of speech skill that enjoys the highest level, after several arts such as dialogue / speaking, rhetoric, presenting the story, and reading the news. Until the debate became a national and international event for the language competition, such as the ASEAN Qatar debate, which enjoys a prestigious position among the activists of the Arabic language. Debate is also of great importance, as it is one of the effective means of communication, where interlocutors cooperate to know the truth and each party discovers what is hidden, and to follow the path of correct inference to reach the truth, and a window for exchanging and competing ideas as well. As for the objectives of this research: (1) To find out how to teach the scientific debate to improve the speaking. (2) To find out the extent of the effectiveness of teaching scientific debate to improve speaking skill for students. In this research, the researcher used the quantitative approach and its quantitative survey type. As for the data analysis, which is to obtain a Pearson product moment correlative coefficient test, with a ratio of 2-tailed sig value = 0.042 > 0.05, it has a correlation between the X variable and the Y variable, the arithmetic pearson correlation = 0.305, and it has a weak relationship. As for the value from the T test, the value of 0,05 > sig 0, 01, then there is an effect of the X variable on the Y variable. For the value of the t-count greater than the t-table, 408,4 > 1,679, there is a significant effect of the X variable on the variable Y. So the conclusion is that “Ha”is accepted and “Ho” is rejected.
 
 
 
 

A Cautionary Note on "A Cautionary Note on the Use of Ornstein Uhlenbeck Models in Macroevolutionary Studies".

Models based on the Ornstein-Uhlenbeck process have become standard for the comparative study of adaptation. Cooper et al. (2016) have cast doubt on this practice by claiming statistical problems with fitting Ornstein-Uhlenbeck models to comparative data. Specifically, they claim that statistical tests of Brownian motion may have too high Type I error rates and that such error rates are exacerbated by measurement error. In this note, we argue that these results have little relevance to the estimation of adaptation with Ornstein-Uhlenbeck models for three reasons. First, we point out that Cooper et al. (2016) did not consider the detection of distinct optima (e.g. for different environments), and therefore did not evaluate the standard test for adaptation. Second, we show that consideration of parameter estimates, and not just statistical significance, will usually lead to correct inferences about evolutionary dynamics. Third, we show that bias due to measurement error can be corrected for by standard methods. We conclude that Cooper et al. (2016) have not identified any statistical problems specific to Ornstein-Uhlenbeck models, and that their cautions against their use in comparative analyses are unfounded and misleading. [adaptation, Ornstein-Uhlenbeck model, phylogenetic comparative method.].

PepGM: A probabilistic graphical model for taxonomic inference of viral proteome samples with associated confidence scores.

Inferring taxonomy in mass spectrometry-based shotgun proteomics is a complex task. In multi-species or viral samples of unknown taxonomic origin, the presence of proteins and corresponding taxa must be inferred from a list of identified peptides which is often complicated by protein homology: many proteins do not only share peptides within a taxon but also between taxa. However, correct taxonomic inference is crucial when identifying different viral strains with high sequence homology-considering, e.g., the different epidemiological characteristics of the various strains of SARS-CoV-2. Additionally, many viruses mutate frequently, further complicating the correct identification of viral proteomic samples. We present PepGM, a probabilistic graphical model for the taxonomic assignment of virus proteomic samples with strain-level resolution and associated confidence scores. PepGM combines the results of a standard proteomic database search algorithm with belief propagation to calculate the marginal distributions, and thus confidence scores, for potential taxonomic assignments. We demonstrate the performance of PepGM using several publicly available virus proteomic datasets, showing its strain-level resolution performance. In two out of eight cases, the taxonomic assignments were only correct on the species level, which PepGM clearly indicates by lower confidence scores. PepGM is written in Python and embedded into a Snakemake workflow. It is available on https://github.com/BAMeScience/PepGM.