ArXiv Paperboy (Stat.ME+Econ.EM)
@paperposterbot.bsky.social
posts updates from arXiv rss feeds for methodology papers in Statistics and Econometrics. Also maintains an arxiv and posts random papers from it.
maintainer: @apoorvalal.com
source code: https://github.com/apoorvalal/bsky_paperbot
maintainer: @apoorvalal.com
source code: https://github.com/apoorvalal/bsky_paperbot
link 📈🤖
An Accurate Computational Approach for Partial Likelihood Using Poisson-Binomial Distributions (Cho, Hong, Du) In a Cox model, the partial likelihood, as the product of a series of conditional probabilities, is used to estimate the regression coefficients. In practice, those conditional p
An Accurate Computational Approach for Partial Likelihood Using Poisson-Binomial Distributions (Cho, Hong, Du) In a Cox model, the partial likelihood, as the product of a series of conditional probabilities, is used to estimate the regression coefficients. In practice, those conditional p
December 27, 2025 at 3:32 AM
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An Accurate Computational Approach for Partial Likelihood Using Poisson-Binomial Distributions (Cho, Hong, Du) In a Cox model, the partial likelihood, as the product of a series of conditional probabilities, is used to estimate the regression coefficients. In practice, those conditional p
An Accurate Computational Approach for Partial Likelihood Using Poisson-Binomial Distributions (Cho, Hong, Du) In a Cox model, the partial likelihood, as the product of a series of conditional probabilities, is used to estimate the regression coefficients. In practice, those conditional p
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Bayesian nonparametric partial clustering: Quantifying the effectiveness of agricultural subsidies across Europe (Mozdzen, Addo, Krisztin et al) The global climate has underscored the need for effective policies to reduce greenhouse gas emissions from all sources, including those resultin
Bayesian nonparametric partial clustering: Quantifying the effectiveness of agricultural subsidies across Europe (Mozdzen, Addo, Krisztin et al) The global climate has underscored the need for effective policies to reduce greenhouse gas emissions from all sources, including those resultin
December 27, 2025 at 1:34 AM
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Bayesian nonparametric partial clustering: Quantifying the effectiveness of agricultural subsidies across Europe (Mozdzen, Addo, Krisztin et al) The global climate has underscored the need for effective policies to reduce greenhouse gas emissions from all sources, including those resultin
Bayesian nonparametric partial clustering: Quantifying the effectiveness of agricultural subsidies across Europe (Mozdzen, Addo, Krisztin et al) The global climate has underscored the need for effective policies to reduce greenhouse gas emissions from all sources, including those resultin
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Forecasting high-dimensional functional time series with dual-factor structures () arXiv:2109.04146v2 Announce Type: replace
Abstract: We propose a dual-factor model for high-dimensional functional time series (HDFTS) that considers multiple populations. The HDFTS is first decomposed int
Forecasting high-dimensional functional time series with dual-factor structures () arXiv:2109.04146v2 Announce Type: replace
Abstract: We propose a dual-factor model for high-dimensional functional time series (HDFTS) that considers multiple populations. The HDFTS is first decomposed int
December 26, 2025 at 10:06 PM
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Forecasting high-dimensional functional time series with dual-factor structures () arXiv:2109.04146v2 Announce Type: replace
Abstract: We propose a dual-factor model for high-dimensional functional time series (HDFTS) that considers multiple populations. The HDFTS is first decomposed int
Forecasting high-dimensional functional time series with dual-factor structures () arXiv:2109.04146v2 Announce Type: replace
Abstract: We propose a dual-factor model for high-dimensional functional time series (HDFTS) that considers multiple populations. The HDFTS is first decomposed int
link 📈🤖
Relative Counterfactual Contrastive Learning for Mitigating Pretrained Stance Bias in Stance Detection () arXiv:2405.10991v1 Announce Type: cross
Abstract: Stance detection classifies stance relations (namely, Favor, Against, or Neither) between comments and targets. Pretrained language
Relative Counterfactual Contrastive Learning for Mitigating Pretrained Stance Bias in Stance Detection () arXiv:2405.10991v1 Announce Type: cross
Abstract: Stance detection classifies stance relations (namely, Favor, Against, or Neither) between comments and targets. Pretrained language
December 26, 2025 at 7:05 PM
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Relative Counterfactual Contrastive Learning for Mitigating Pretrained Stance Bias in Stance Detection () arXiv:2405.10991v1 Announce Type: cross
Abstract: Stance detection classifies stance relations (namely, Favor, Against, or Neither) between comments and targets. Pretrained language
Relative Counterfactual Contrastive Learning for Mitigating Pretrained Stance Bias in Stance Detection () arXiv:2405.10991v1 Announce Type: cross
Abstract: Stance detection classifies stance relations (namely, Favor, Against, or Neither) between comments and targets. Pretrained language
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Optimal Treatment Regimes for Proximal Causal Learning () A common concern when a policymaker draws causal inferences from and makes
decisions based on observational data is that the measured covariates are
insufficiently rich to account for all sources of confounding, i.e., the
standard
Optimal Treatment Regimes for Proximal Causal Learning () A common concern when a policymaker draws causal inferences from and makes
decisions based on observational data is that the measured covariates are
insufficiently rich to account for all sources of confounding, i.e., the
standard
December 26, 2025 at 4:07 PM
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Optimal Treatment Regimes for Proximal Causal Learning () A common concern when a policymaker draws causal inferences from and makes
decisions based on observational data is that the measured covariates are
insufficiently rich to account for all sources of confounding, i.e., the
standard
Optimal Treatment Regimes for Proximal Causal Learning () A common concern when a policymaker draws causal inferences from and makes
decisions based on observational data is that the measured covariates are
insufficiently rich to account for all sources of confounding, i.e., the
standard
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A Bayesian Ensemble Projection of Climate Change and Technological Impacts on Future Crop Yields (Li, Kitsios, Newth et al) This paper introduces a Bayesian hierarchical modeling framework within a fully probabilistic setting for crop yield estimation, model selection, and uncertainty for
A Bayesian Ensemble Projection of Climate Change and Technological Impacts on Future Crop Yields (Li, Kitsios, Newth et al) This paper introduces a Bayesian hierarchical modeling framework within a fully probabilistic setting for crop yield estimation, model selection, and uncertainty for
December 26, 2025 at 3:33 AM
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A Bayesian Ensemble Projection of Climate Change and Technological Impacts on Future Crop Yields (Li, Kitsios, Newth et al) This paper introduces a Bayesian hierarchical modeling framework within a fully probabilistic setting for crop yield estimation, model selection, and uncertainty for
A Bayesian Ensemble Projection of Climate Change and Technological Impacts on Future Crop Yields (Li, Kitsios, Newth et al) This paper introduces a Bayesian hierarchical modeling framework within a fully probabilistic setting for crop yield estimation, model selection, and uncertainty for
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A graphical multi-fidelity Gaussian process model, with application to emulation of heavy-ion collisions () With advances in scientific computing and mathematical modeling, complex
scientific phenomena such as galaxy formations and rocket propulsion can now be
reliably simulated. Such sim
A graphical multi-fidelity Gaussian process model, with application to emulation of heavy-ion collisions () With advances in scientific computing and mathematical modeling, complex
scientific phenomena such as galaxy formations and rocket propulsion can now be
reliably simulated. Such sim
December 26, 2025 at 1:34 AM
link 📈🤖
A graphical multi-fidelity Gaussian process model, with application to emulation of heavy-ion collisions () With advances in scientific computing and mathematical modeling, complex
scientific phenomena such as galaxy formations and rocket propulsion can now be
reliably simulated. Such sim
A graphical multi-fidelity Gaussian process model, with application to emulation of heavy-ion collisions () With advances in scientific computing and mathematical modeling, complex
scientific phenomena such as galaxy formations and rocket propulsion can now be
reliably simulated. Such sim
link 📈🤖
On the Parameter Identifiability of Partially Observed Linear Causal Models () arXiv:2407.16975v1 Announce Type: cross
Abstract: Linear causal models are important tools for modeling causal dependencies and yet in practice, only a subset of the variables can be observed. In this paper, w
On the Parameter Identifiability of Partially Observed Linear Causal Models () arXiv:2407.16975v1 Announce Type: cross
Abstract: Linear causal models are important tools for modeling causal dependencies and yet in practice, only a subset of the variables can be observed. In this paper, w
December 25, 2025 at 10:06 PM
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On the Parameter Identifiability of Partially Observed Linear Causal Models () arXiv:2407.16975v1 Announce Type: cross
Abstract: Linear causal models are important tools for modeling causal dependencies and yet in practice, only a subset of the variables can be observed. In this paper, w
On the Parameter Identifiability of Partially Observed Linear Causal Models () arXiv:2407.16975v1 Announce Type: cross
Abstract: Linear causal models are important tools for modeling causal dependencies and yet in practice, only a subset of the variables can be observed. In this paper, w
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Topological Detection of Phenomenological Bifurcations with Unreliable Kernel Densities () Phenomenological (P-type) bifurcations are qualitative changes in stochastic dynamical systems whereby the stationary probability density function (PDF) changes its topology. The current state of th
Topological Detection of Phenomenological Bifurcations with Unreliable Kernel Densities () Phenomenological (P-type) bifurcations are qualitative changes in stochastic dynamical systems whereby the stationary probability density function (PDF) changes its topology. The current state of th
December 25, 2025 at 7:05 PM
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Topological Detection of Phenomenological Bifurcations with Unreliable Kernel Densities () Phenomenological (P-type) bifurcations are qualitative changes in stochastic dynamical systems whereby the stationary probability density function (PDF) changes its topology. The current state of th
Topological Detection of Phenomenological Bifurcations with Unreliable Kernel Densities () Phenomenological (P-type) bifurcations are qualitative changes in stochastic dynamical systems whereby the stationary probability density function (PDF) changes its topology. The current state of th
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Closed-form empirical Bernstein confidence sequences for scalars and matrices (Chugg, Ramdas) We derive a new closed-form variance-adaptive confidence sequence (CS) for estimating the average conditional mean of a sequence of bounded random variables. Empirically, it yields the tightest c
Closed-form empirical Bernstein confidence sequences for scalars and matrices (Chugg, Ramdas) We derive a new closed-form variance-adaptive confidence sequence (CS) for estimating the average conditional mean of a sequence of bounded random variables. Empirically, it yields the tightest c
December 25, 2025 at 4:50 PM
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Closed-form empirical Bernstein confidence sequences for scalars and matrices (Chugg, Ramdas) We derive a new closed-form variance-adaptive confidence sequence (CS) for estimating the average conditional mean of a sequence of bounded random variables. Empirically, it yields the tightest c
Closed-form empirical Bernstein confidence sequences for scalars and matrices (Chugg, Ramdas) We derive a new closed-form variance-adaptive confidence sequence (CS) for estimating the average conditional mean of a sequence of bounded random variables. Empirically, it yields the tightest c
link 📈🤖
LLM Personas as a Substitute for Field Experiments in Method Benchmarking (Kang) Field experiments (A/B tests) are often the most credible benchmark for methods in societal systems, but their cost and latency create a major bottleneck for iterative method development. LLM-based persona si
LLM Personas as a Substitute for Field Experiments in Method Benchmarking (Kang) Field experiments (A/B tests) are often the most credible benchmark for methods in societal systems, but their cost and latency create a major bottleneck for iterative method development. LLM-based persona si
December 25, 2025 at 4:46 PM
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LLM Personas as a Substitute for Field Experiments in Method Benchmarking (Kang) Field experiments (A/B tests) are often the most credible benchmark for methods in societal systems, but their cost and latency create a major bottleneck for iterative method development. LLM-based persona si
LLM Personas as a Substitute for Field Experiments in Method Benchmarking (Kang) Field experiments (A/B tests) are often the most credible benchmark for methods in societal systems, but their cost and latency create a major bottleneck for iterative method development. LLM-based persona si
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Can Agentic AI Match the Performance of Human Data Scientists? (Luo, Du, Tian et al) Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large language models (LLMs) have significantly automated data scie
Can Agentic AI Match the Performance of Human Data Scientists? (Luo, Du, Tian et al) Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large language models (LLMs) have significantly automated data scie
December 25, 2025 at 4:45 PM
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Can Agentic AI Match the Performance of Human Data Scientists? (Luo, Du, Tian et al) Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large language models (LLMs) have significantly automated data scie
Can Agentic AI Match the Performance of Human Data Scientists? (Luo, Du, Tian et al) Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large language models (LLMs) have significantly automated data scie
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Diffusion Models in Simulation-Based Inference: A Tutorial Review (Arruda, Bracher, K\"othe et al) Diffusion models have recently emerged as powerful learners for simulation-based inference (SBI), enabling fast and accurate estimation of latent parameters from simulated and real data. The
Diffusion Models in Simulation-Based Inference: A Tutorial Review (Arruda, Bracher, K\"othe et al) Diffusion models have recently emerged as powerful learners for simulation-based inference (SBI), enabling fast and accurate estimation of latent parameters from simulated and real data. The
December 25, 2025 at 4:44 PM
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Diffusion Models in Simulation-Based Inference: A Tutorial Review (Arruda, Bracher, K\"othe et al) Diffusion models have recently emerged as powerful learners for simulation-based inference (SBI), enabling fast and accurate estimation of latent parameters from simulated and real data. The
Diffusion Models in Simulation-Based Inference: A Tutorial Review (Arruda, Bracher, K\"othe et al) Diffusion models have recently emerged as powerful learners for simulation-based inference (SBI), enabling fast and accurate estimation of latent parameters from simulated and real data. The
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Proximal Survival Analysis for Dependent Left Truncation (Wang, Ying, Xu) In prevalent cohort studies with delayed entry, time-to-event outcomes are often subject to left truncation where only subjects that have not experienced the event at study entry are included, leading to selection b
Proximal Survival Analysis for Dependent Left Truncation (Wang, Ying, Xu) In prevalent cohort studies with delayed entry, time-to-event outcomes are often subject to left truncation where only subjects that have not experienced the event at study entry are included, leading to selection b
December 25, 2025 at 4:39 PM
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Proximal Survival Analysis for Dependent Left Truncation (Wang, Ying, Xu) In prevalent cohort studies with delayed entry, time-to-event outcomes are often subject to left truncation where only subjects that have not experienced the event at study entry are included, leading to selection b
Proximal Survival Analysis for Dependent Left Truncation (Wang, Ying, Xu) In prevalent cohort studies with delayed entry, time-to-event outcomes are often subject to left truncation where only subjects that have not experienced the event at study entry are included, leading to selection b
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Difference-in-Differences in the Presence of Unknown Interference (Mealli, Viviens) The stable unit treatment value (SUTVA) is a crucial assumption in the Difference-in-Differences (DiD) research design. It rules out hidden versions of treatment and any sort of interference and spillover
Difference-in-Differences in the Presence of Unknown Interference (Mealli, Viviens) The stable unit treatment value (SUTVA) is a crucial assumption in the Difference-in-Differences (DiD) research design. It rules out hidden versions of treatment and any sort of interference and spillover
December 25, 2025 at 4:37 PM
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Difference-in-Differences in the Presence of Unknown Interference (Mealli, Viviens) The stable unit treatment value (SUTVA) is a crucial assumption in the Difference-in-Differences (DiD) research design. It rules out hidden versions of treatment and any sort of interference and spillover
Difference-in-Differences in the Presence of Unknown Interference (Mealli, Viviens) The stable unit treatment value (SUTVA) is a crucial assumption in the Difference-in-Differences (DiD) research design. It rules out hidden versions of treatment and any sort of interference and spillover
link 📈🤖
Modeling gap acceptance behavior allowing for perceptual distortions and exogenous influences (Sharma, Chakroborty, Chakraborty) This work on gap acceptance is based on the premise that the decision to accept/reject a gap happens in a person's mind and therefore must be based on the perce
Modeling gap acceptance behavior allowing for perceptual distortions and exogenous influences (Sharma, Chakroborty, Chakraborty) This work on gap acceptance is based on the premise that the decision to accept/reject a gap happens in a person's mind and therefore must be based on the perce
December 25, 2025 at 4:35 PM
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Modeling gap acceptance behavior allowing for perceptual distortions and exogenous influences (Sharma, Chakroborty, Chakraborty) This work on gap acceptance is based on the premise that the decision to accept/reject a gap happens in a person's mind and therefore must be based on the perce
Modeling gap acceptance behavior allowing for perceptual distortions and exogenous influences (Sharma, Chakroborty, Chakraborty) This work on gap acceptance is based on the premise that the decision to accept/reject a gap happens in a person's mind and therefore must be based on the perce
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Measuring Variable Importance via Accumulated Local Effects (Zhu, Apley) A shortcoming of black-box supervised learning models is their lack of interpretability or transparency. To facilitate interpretation, post-hoc global variable importance measures (VIMs) are widely used to assign to
Measuring Variable Importance via Accumulated Local Effects (Zhu, Apley) A shortcoming of black-box supervised learning models is their lack of interpretability or transparency. To facilitate interpretation, post-hoc global variable importance measures (VIMs) are widely used to assign to
December 25, 2025 at 4:31 PM
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Measuring Variable Importance via Accumulated Local Effects (Zhu, Apley) A shortcoming of black-box supervised learning models is their lack of interpretability or transparency. To facilitate interpretation, post-hoc global variable importance measures (VIMs) are widely used to assign to
Measuring Variable Importance via Accumulated Local Effects (Zhu, Apley) A shortcoming of black-box supervised learning models is their lack of interpretability or transparency. To facilitate interpretation, post-hoc global variable importance measures (VIMs) are widely used to assign to
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Two-level D- and A-optimal designs of Ehlich type with run sizes three more than a multiple of four (Hameed, Schoen, Ares et al) For the majority of run sizes N where N <= 20, the literature reports the best D- and A-optimal designs for the main-effects model which sequentially minimizes
Two-level D- and A-optimal designs of Ehlich type with run sizes three more than a multiple of four (Hameed, Schoen, Ares et al) For the majority of run sizes N where N <= 20, the literature reports the best D- and A-optimal designs for the main-effects model which sequentially minimizes
December 25, 2025 at 4:27 PM
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Two-level D- and A-optimal designs of Ehlich type with run sizes three more than a multiple of four (Hameed, Schoen, Ares et al) For the majority of run sizes N where N <= 20, the literature reports the best D- and A-optimal designs for the main-effects model which sequentially minimizes
Two-level D- and A-optimal designs of Ehlich type with run sizes three more than a multiple of four (Hameed, Schoen, Ares et al) For the majority of run sizes N where N <= 20, the literature reports the best D- and A-optimal designs for the main-effects model which sequentially minimizes
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Learning the Macroeconomic Language (Chib, Tan) We show how state-of-the-art large language models (LLMs), seemingly inapplicable to the small samples typical of macroeconomics, can be trained to learn the language of macroeconomy. We estimate a large-scale dynamic stochastic general equi
Learning the Macroeconomic Language (Chib, Tan) We show how state-of-the-art large language models (LLMs), seemingly inapplicable to the small samples typical of macroeconomics, can be trained to learn the language of macroeconomy. We estimate a large-scale dynamic stochastic general equi
December 25, 2025 at 4:26 PM
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Learning the Macroeconomic Language (Chib, Tan) We show how state-of-the-art large language models (LLMs), seemingly inapplicable to the small samples typical of macroeconomics, can be trained to learn the language of macroeconomy. We estimate a large-scale dynamic stochastic general equi
Learning the Macroeconomic Language (Chib, Tan) We show how state-of-the-art large language models (LLMs), seemingly inapplicable to the small samples typical of macroeconomics, can be trained to learn the language of macroeconomy. We estimate a large-scale dynamic stochastic general equi
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A Unified Inference Method for FROC-type Curves and Related Summary Indices (Sun, Liu, Zhou) Free-response observer performance studies are of great importance for accuracy evaluation and comparison in tasks related to the detection and localization of multiple targets or signals. The fre
A Unified Inference Method for FROC-type Curves and Related Summary Indices (Sun, Liu, Zhou) Free-response observer performance studies are of great importance for accuracy evaluation and comparison in tasks related to the detection and localization of multiple targets or signals. The fre
December 25, 2025 at 4:22 PM
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A Unified Inference Method for FROC-type Curves and Related Summary Indices (Sun, Liu, Zhou) Free-response observer performance studies are of great importance for accuracy evaluation and comparison in tasks related to the detection and localization of multiple targets or signals. The fre
A Unified Inference Method for FROC-type Curves and Related Summary Indices (Sun, Liu, Zhou) Free-response observer performance studies are of great importance for accuracy evaluation and comparison in tasks related to the detection and localization of multiple targets or signals. The fre
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Welfare at Risk: Distributional impact of policy interventions (Lambros, Melo) This paper proposes a framewrok for analyzing how the welfare effects of policy interventions are distributed across individuals when those effects are unobserved. Rather than focusing solely on average outcome
Welfare at Risk: Distributional impact of policy interventions (Lambros, Melo) This paper proposes a framewrok for analyzing how the welfare effects of policy interventions are distributed across individuals when those effects are unobserved. Rather than focusing solely on average outcome
December 25, 2025 at 4:17 PM
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Welfare at Risk: Distributional impact of policy interventions (Lambros, Melo) This paper proposes a framewrok for analyzing how the welfare effects of policy interventions are distributed across individuals when those effects are unobserved. Rather than focusing solely on average outcome
Welfare at Risk: Distributional impact of policy interventions (Lambros, Melo) This paper proposes a framewrok for analyzing how the welfare effects of policy interventions are distributed across individuals when those effects are unobserved. Rather than focusing solely on average outcome
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Testing Exclusion and Shape Restrictions in Potential Outcomes Models (Kaido, Ponomarev) Exclusion and shape restrictions play a central role in defining causal effects and interpreting estimates in potential outcomes models. To date, the testable implications of such restrictions have be
Testing Exclusion and Shape Restrictions in Potential Outcomes Models (Kaido, Ponomarev) Exclusion and shape restrictions play a central role in defining causal effects and interpreting estimates in potential outcomes models. To date, the testable implications of such restrictions have be
December 25, 2025 at 4:13 PM
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Testing Exclusion and Shape Restrictions in Potential Outcomes Models (Kaido, Ponomarev) Exclusion and shape restrictions play a central role in defining causal effects and interpreting estimates in potential outcomes models. To date, the testable implications of such restrictions have be
Testing Exclusion and Shape Restrictions in Potential Outcomes Models (Kaido, Ponomarev) Exclusion and shape restrictions play a central role in defining causal effects and interpreting estimates in potential outcomes models. To date, the testable implications of such restrictions have be
link 📈🤖
Improving optimal subsampling through stratification (Yang, Lumley, Shepherd et al) Recent works have proposed optimal subsampling algorithms to improve computational efficiency in large datasets and to design validation studies in the presence of measurement error. Existing approaches ge
Improving optimal subsampling through stratification (Yang, Lumley, Shepherd et al) Recent works have proposed optimal subsampling algorithms to improve computational efficiency in large datasets and to design validation studies in the presence of measurement error. Existing approaches ge
December 25, 2025 at 4:09 PM
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Improving optimal subsampling through stratification (Yang, Lumley, Shepherd et al) Recent works have proposed optimal subsampling algorithms to improve computational efficiency in large datasets and to design validation studies in the presence of measurement error. Existing approaches ge
Improving optimal subsampling through stratification (Yang, Lumley, Shepherd et al) Recent works have proposed optimal subsampling algorithms to improve computational efficiency in large datasets and to design validation studies in the presence of measurement error. Existing approaches ge
link 📈🤖
The Whittle likelihood for mixed models with application to groundwater level time series (Pypkowski, Sykulski, Martin et al) Understanding the processes that influence groundwater levels is crucial for forecasting and responding to hazards such as groundwater droughts. Mixed models, whic
The Whittle likelihood for mixed models with application to groundwater level time series (Pypkowski, Sykulski, Martin et al) Understanding the processes that influence groundwater levels is crucial for forecasting and responding to hazards such as groundwater droughts. Mixed models, whic
December 25, 2025 at 4:07 PM
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The Whittle likelihood for mixed models with application to groundwater level time series (Pypkowski, Sykulski, Martin et al) Understanding the processes that influence groundwater levels is crucial for forecasting and responding to hazards such as groundwater droughts. Mixed models, whic
The Whittle likelihood for mixed models with application to groundwater level time series (Pypkowski, Sykulski, Martin et al) Understanding the processes that influence groundwater levels is crucial for forecasting and responding to hazards such as groundwater droughts. Mixed models, whic
link 📈🤖
Enhancing the Tensor Normal via Geometrically Parameterized Cholesky Factors (Simonis, Wells) In this article, we explore Bayesian extensions of the tensor normal model through a geometric expansion of the multi-way covariance's Cholesky factor inspired by the Fr\'echet mean under the log
Enhancing the Tensor Normal via Geometrically Parameterized Cholesky Factors (Simonis, Wells) In this article, we explore Bayesian extensions of the tensor normal model through a geometric expansion of the multi-way covariance's Cholesky factor inspired by the Fr\'echet mean under the log
December 25, 2025 at 3:34 AM
link 📈🤖
Enhancing the Tensor Normal via Geometrically Parameterized Cholesky Factors (Simonis, Wells) In this article, we explore Bayesian extensions of the tensor normal model through a geometric expansion of the multi-way covariance's Cholesky factor inspired by the Fr\'echet mean under the log
Enhancing the Tensor Normal via Geometrically Parameterized Cholesky Factors (Simonis, Wells) In this article, we explore Bayesian extensions of the tensor normal model through a geometric expansion of the multi-way covariance's Cholesky factor inspired by the Fr\'echet mean under the log