This document discusses statistics, computation, and software engineering challenges in developing and maintaining mixed modeling software in R. It covers:
- Definitions of generalized linear mixed models (GLMMs) and examples of their applications
- Statistical challenges including maximum likelihood estimation, inference using computational and Bayesian methods
- Computational challenges of large, sparse matrices and bounded optimization
- Software engineering tradeoffs between high-level languages like R and low-level languages like C++
The overall goal is to discuss statistical, computational and software challenges in building mixed modeling software.
Consideration on Fairness-aware Data Mining
IEEE International Workshop on Discrimination and Privacy-Aware Data Mining (DPADM 2012)
Dec. 10, 2012 @ Brussels, Belgium, in conjunction with ICDM2012
Article @ Official Site: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2012.101
Article @ Personal Site: http://www.kamishima.net/archive/2012-ws-icdm-print.pdf
Handnote: http://www.kamishima.net/archive/2012-ws-icdm-HN.pdf
Workshop Homepage: https://sites.google.com/site/dpadm2012/
Abstract:
With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair regarding sensitive features such as race, gender, religion, and so on. Several researchers have recently begun to develop fairness-aware or discrimination-aware data mining techniques that take into account issues of social fairness, discrimination, and neutrality. In this paper, after demonstrating the applications of these techniques, we explore the formal concepts of fairness and techniques for handling fairness in data mining. We then provide an integrated view of these concepts based on statistical independence. Finally, we discuss the relations between fairness-aware data mining and other research topics, such as privacy-preserving data mining or causal inference.
Consideration on Fairness-aware Data Mining
IEEE International Workshop on Discrimination and Privacy-Aware Data Mining (DPADM 2012)
Dec. 10, 2012 @ Brussels, Belgium, in conjunction with ICDM2012
Article @ Official Site: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2012.101
Article @ Personal Site: http://www.kamishima.net/archive/2012-ws-icdm-print.pdf
Handnote: http://www.kamishima.net/archive/2012-ws-icdm-HN.pdf
Workshop Homepage: https://sites.google.com/site/dpadm2012/
Abstract:
With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair regarding sensitive features such as race, gender, religion, and so on. Several researchers have recently begun to develop fairness-aware or discrimination-aware data mining techniques that take into account issues of social fairness, discrimination, and neutrality. In this paper, after demonstrating the applications of these techniques, we explore the formal concepts of fairness and techniques for handling fairness in data mining. We then provide an integrated view of these concepts based on statistical independence. Finally, we discuss the relations between fairness-aware data mining and other research topics, such as privacy-preserving data mining or causal inference.
Susie Bayarri Plenary Lecture given in the ISBA (International Society of Bayesian Analysis) World Meeting in Montreal, Canada on June 30, 2022, by Pierre E, Jacob (https://sites.google.com/site/pierrejacob/)
Towards reproducibility and maximally-open dataPablo Bernabeu
Presented at the Open Scholarship Prize Competition 2021, organised by Open Scholarship Community Galway.
Video of the presentation: https://nuigalway.mediaspace.kaltura.com/media/OSW2021A+OSCG+Open+Scholarship+Prize+-+The+Final!/1_d7ekd3d3/121659351#t=56:08
An introduction to bioinformatics practices and aims will be given and contrasted against approaches from other fields. Most importantly, it will be discussed how bioinformatics fits into the discovery cycle for hypothesis driven neuroscience research.
We are excited to announce and demonstrate some new and highly requested features in this webcast, including predicting phenotypes by applying existing GBLUP or Bayesian models and meta-analysis for GWAS studies.
Recently in SVS we added additional genomic prediction tools such as Bayesian Genomic Prediction and K-Fold Cross Validation. We have continued to build out the prediction suite of tools by adding the ability to apply the results of a model to a new genomic dataset to predict the phenotype. This is designed to work hand-in-hand with the output of K-Fold cross-validation using either GBLUP or Bayes C/C-pi.
Next we will provide a sneak-peak at the upcoming meta-analysis feature. This was one of the most requested features in our latest customer survey. Including this feature in SVS will combine the power of the numerous file imports and data visualization with the standard meta-analysis methods in use today.
Join us as we explore how these new features can stream-line your analysis and provide additional insight into your results.
Susie Bayarri Plenary Lecture given in the ISBA (International Society of Bayesian Analysis) World Meeting in Montreal, Canada on June 30, 2022, by Pierre E, Jacob (https://sites.google.com/site/pierrejacob/)
Towards reproducibility and maximally-open dataPablo Bernabeu
Presented at the Open Scholarship Prize Competition 2021, organised by Open Scholarship Community Galway.
Video of the presentation: https://nuigalway.mediaspace.kaltura.com/media/OSW2021A+OSCG+Open+Scholarship+Prize+-+The+Final!/1_d7ekd3d3/121659351#t=56:08
An introduction to bioinformatics practices and aims will be given and contrasted against approaches from other fields. Most importantly, it will be discussed how bioinformatics fits into the discovery cycle for hypothesis driven neuroscience research.
We are excited to announce and demonstrate some new and highly requested features in this webcast, including predicting phenotypes by applying existing GBLUP or Bayesian models and meta-analysis for GWAS studies.
Recently in SVS we added additional genomic prediction tools such as Bayesian Genomic Prediction and K-Fold Cross Validation. We have continued to build out the prediction suite of tools by adding the ability to apply the results of a model to a new genomic dataset to predict the phenotype. This is designed to work hand-in-hand with the output of K-Fold cross-validation using either GBLUP or Bayes C/C-pi.
Next we will provide a sneak-peak at the upcoming meta-analysis feature. This was one of the most requested features in our latest customer survey. Including this feature in SVS will combine the power of the numerous file imports and data visualization with the standard meta-analysis methods in use today.
Join us as we explore how these new features can stream-line your analysis and provide additional insight into your results.
We are excited to announce and demonstrate some new and highly requested features in this webcast, including predicting phenotypes by applying existing GBLUP or Bayesian models and meta-analysis for GWAS studies.
Why Do Computational Scientists Trust Their Sojpipitone
A very informal talk I gave to Hausi Muller's group at UVic in June 2009.
I have included, without permission, slides from Daniel Hook's excellent presentation at SE-CSE 2009 (http://www.cs.ua.edu/~SECSE09/schedule.htm).
Computational Approaches to Systems BiologyMike Hucka
Presentation given at the Sydney Computational Biologists meetup on 21 August 2013 (http://australianbioinformatics.net/past-events/2013/8/21/computational-approaches-to-systems-biology.html).
Scott Lundberg, Microsoft Research - Explainable Machine Learning with Shaple...Sri Ambati
This session was recorded in NYC on October 22nd, 2019 and can be viewed here: https://youtu.be/ngOBhhINWb8
Explainable Machine Learning with Shapley Values
Shapley values are popular approach for explaining predictions made by complex machine learning models. In this talk I will discuss what problems Shapley values solve, an intuitive presentation of what they mean, and examples of how they can be used through the ‘shap’ python package.
Bio: I am a senior researcher at Microsoft Research. Before joining Microsoft, I did my Ph.D. studies at the Paul G. Allen School of Computer Science & Engineering of the University of Washington working with Su-In Lee. My work focuses on explainable artificial intelligence and its application to problems in medicine and healthcare. This has led to the development of broadly applicable methods and tools for interpreting complex machine learning models that are now used in banking, logistics, sports, manufacturing, cloud services, economics, and many other areas.
Contents of the presentation:
• GA – Introduction
• GA – Fundamentals
• GA – Genotype Representation
• GA – Population
• GA – Fitness Function
• GA – Parent Selection
• GA – Crossover
• GA – Mutation
• Research Paper
Ecological synthesis across scales: West Nile virus in individuals and commun...Ben Bolker
West Nile Virus (WNV), a mosquito-borne virus of birds, emerged in North America in 1999; the invading strain was then displaced within a few years by a novel mutant. In order to understand this competitive displacement event, and to predict transmission of WNV in bird communities comprising hundreds of species, we collected data on bird and mosquito infections, bird community composition, and mosquito biting preferences from lab experiments, field observations, and citizen-science databases. We use a Bayesian framework, including a method for phylogenetic imputation applied to species with missing data, to synthesize information across the entire disease life cycle and throughout the community.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
4. Denitions
Statistics
Computation
Software
Conclusions
References
(Generalized) linear mixed models
(G)LMMs: a statistical modeling framework incorporating:
Linear combinations of categorical and continuous
predictors, and interactions
Response distributions in the
exponential family
(binomial, Poisson, and extensions)
Any smooth, monotonic
link function
(e.g. logistic, exponential models)
Flexible combinations of
blocking factors
(clustering; random eects)
Applications in ecology, neurobiology, behaviour, epidemiology, real
estate, . . .
Ben Bolker
Mixed model software
5. Denitions
Statistics
Computation
Software
Conclusions
References
(Generalized) linear mixed models
(G)LMMs: a statistical modeling framework incorporating:
Linear combinations of categorical and continuous
predictors, and interactions
Response distributions in the
exponential family
(binomial, Poisson, and extensions)
Any smooth, monotonic
link function
(e.g. logistic, exponential models)
Flexible combinations of
blocking factors
(clustering; random eects)
Applications in ecology, neurobiology, behaviour, epidemiology, real
estate, . . .
Ben Bolker
Mixed model software
6. Denitions
Statistics
Computation
Software
Conclusions
References
(Generalized) linear mixed models
(G)LMMs: a statistical modeling framework incorporating:
Linear combinations of categorical and continuous
predictors, and interactions
Response distributions in the
exponential family
(binomial, Poisson, and extensions)
Any smooth, monotonic
link function
(e.g. logistic, exponential models)
Flexible combinations of
blocking factors
(clustering; random eects)
Applications in ecology, neurobiology, behaviour, epidemiology, real
estate, . . .
Ben Bolker
Mixed model software
7. Denitions
Statistics
Computation
Software
Conclusions
References
(Generalized) linear mixed models
(G)LMMs: a statistical modeling framework incorporating:
Linear combinations of categorical and continuous
predictors, and interactions
Response distributions in the
exponential family
(binomial, Poisson, and extensions)
Any smooth, monotonic
link function
(e.g. logistic, exponential models)
Flexible combinations of
blocking factors
(clustering; random eects)
Applications in ecology, neurobiology, behaviour, epidemiology, real
estate, . . .
Ben Bolker
Mixed model software
8. Denitions
Statistics
Computation
Software
Conclusions
References
Examples
ecology survival, predation, etc. (experimental plots)
genomics presence/absence of polymorphisms, gene expression
(individuals)
educational assessment student scores (students
×
teachers)
psychology/sensometrics decisions, responses to stimuli
(individuals)
epidemiology disease prevalence (postal codes, provinces, countries)
Ben Bolker
Mixed model software
9. Denitions
Statistics
Computation
Software
Conclusions
References
Examples
ecology survival, predation, etc. (experimental plots)
genomics presence/absence of polymorphisms, gene expression
(individuals)
educational assessment student scores (students
×
teachers)
psychology/sensometrics decisions, responses to stimuli
(individuals)
epidemiology disease prevalence (postal codes, provinces, countries)
Ben Bolker
Mixed model software
10. Denitions
Statistics
Computation
Software
Conclusions
References
Examples
ecology survival, predation, etc. (experimental plots)
genomics presence/absence of polymorphisms, gene expression
(individuals)
educational assessment student scores (students
×
teachers)
psychology/sensometrics decisions, responses to stimuli
(individuals)
epidemiology disease prevalence (postal codes, provinces, countries)
Ben Bolker
Mixed model software
11. Denitions
Statistics
Computation
Software
Conclusions
References
Examples
ecology survival, predation, etc. (experimental plots)
genomics presence/absence of polymorphisms, gene expression
(individuals)
educational assessment student scores (students
×
teachers)
psychology/sensometrics decisions, responses to stimuli
(individuals)
epidemiology disease prevalence (postal codes, provinces, countries)
Ben Bolker
Mixed model software
12. Denitions
Statistics
Computation
Software
Conclusions
References
Examples
ecology survival, predation, etc. (experimental plots)
genomics presence/absence of polymorphisms, gene expression
(individuals)
educational assessment student scores (students
×
teachers)
psychology/sensometrics decisions, responses to stimuli
(individuals)
epidemiology disease prevalence (postal codes, provinces, countries)
Ben Bolker
Mixed model software
21. Denitions
Statistics
Computation
Software
Problems of big data
How big is big?
Airline data: 12G
(G)LMM works on
moderately large problems,
e.g. student evaluations
(≈ 75K total, 3K students, 1K profs)
Fairly clever linear algebra
Possible improvements?
Chunking/parallelization
Out-of-memory operation
Ben Bolker
Mixed model software
Conclusions
References
22. Denitions
Statistics
Computation
Sparse matrix algorithms
repeated decomposition of
large, matrices (especially Z )
ll-reducing permutation to
improve sparsity pattern
further improvements possible:
better matrix representation,
parallelization?
Ben Bolker
Mixed model software
Software
Conclusions
References
26. Denitions
Statistics
Computation
Software
Getting it right vs. getting it written
the curse of neophilia: Superiority
many versions:
nlme, lme4(a,b,Eigen)
The moral of the story is that if
you want to create a beautiful
language, for god's sake don't
make it useful
(Patrick Burns)
Ben Bolker
Mixed model software
...
Conclusions
References
27. Denitions
Statistics
Computation
Software
Conclusions
References
Sociological issues
Wide user base:
As usual when software for complicated statistical
inference procedures is broadly disseminated, there is
potential for abuse and misinterpretation.
(Breslow, 2004)
What if there is no good answer?
do no harm vs. better me than someone else
Diagnostics and warning messages
End users
Ben Bolker
Mixed model software
vs.
downstream developers
32. Denitions
Statistics
Computation
Software
Conclusions
References
Booth, J.G. and Hobert, J.P., 1999. Journal of the Royal Statistical Society. Series B, 61(1):265285.
doi:10.1111/1467- 9868.00176.
Breslow, N.E., 2004. In D.Y. Lin and P.J. Heagerty, editors, Proceedings of the second Seattle
symposium in biostatistics: Analysis of correlated data, pages 122. Springer. ISBN 0387208623.
McKeon, C.S., Stier, A., et al., 2012. Oecologia, 169(4):10951103. ISSN 0029-8549.
doi:10.1007/s00442- 012-2275-2.
Pinheiro, J.C. and Bates, D.M., 1996. Statistics and Computing, 6(3):289296.
doi:10.1007/BF00140873.
Ponciano, J.M., Taper, M.L., et al., 2009. Ecology, 90(2):356362. ISSN 0012-9658.
Sung, Y.J., 2007. The Annals of Statistics, 35(3):9901011. ISSN 0090-5364.
doi:10.1214/009053606000001389.
Ben Bolker
Mixed model software