'estimation' 검색 결과

Combine laboratory animal welfare measurements into reference-relative RELSA severity scores. Analyze individual trajectories, explore forecasts for specified observation times, and derive candidate score zones, with explicit checks on normalization, reference selection, prediction intervals, and evaluation limits.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Combine animal welfare measurements into daily scores
• Compare group severity using a shared reference
• Explore severity forecasts at specified observation times

Build and optimize quantum circuits with PennyLane, including models that combine quantum circuits with PyTorch or JAX. Explore molecular energy estimation and optimization problems, validate values and gradients in simulation, and prepare tested circuits for provider plugins.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check quantum circuit values and gradients in simulation
• Train hybrid quantum and PyTorch or JAX models
• Estimate molecular energies with VQE

Calculate seawater carbonate chemistry from two independent measurements using PyCO2SYS. Produce pH, carbonate species, and mineral saturation results, convert laboratory conditions to ocean conditions, and propagate measurement uncertainty while recording calculation assumptions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Calculate carbonate chemistry from paired seawater measurements
• Assess aragonite and calcite saturation
• Convert laboratory results to ocean temperature and pressure

Retrieve current Azure retail prices and estimate monthly or annual costs by service, region, and configuration. Compare payment options and calculate Copilot Studio credit usage and estimated costs from expected agent activity.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Estimate monthly and annual Azure costs
• Compare prices across regions and configurations
• Compare reservations, savings plans, and spot pricing

Model trading costs and execution assumptions in backtests, including slippage, the gap between expected and simulated fill prices. Compare price impact, fees, execution delays, and order splitting to assess how assumptions affect strategy results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare backtests under different slippage assumptions
• Estimate how trading costs reduce annual returns
• Explore VWAP and TWAP order splitting

Scan a codebase for technical debt—issues that make future maintenance harder—and prioritize fixes using business impact and effort. Generate a ranked backlog, sprint allocations, and trend reports to assess cleanup work over time.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assess technical debt across a codebase
• Prioritize refactoring for the next sprint
• Compare debt levels before and after cleanup

Design product pricing based on customer value, competitor offerings, and willingness to pay. Compare charging models, define plan tiers and included features, and outline experiments to test prices and assumptions before introducing a new pricing structure.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting prices for a new product
• Comparing flat-rate and usage-based pricing
• Designing free and paid plan tiers

Build a sprint plan for a short development cycle using team availability and recent delivery history. Select work that fits capacity, sequence dependent tasks, identify owners and risks, and reserve room for unexpected work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing a sprint planning meeting
• Estimating capacity around leave and other duties
• Selecting backlog work that fits team capacity

Investigate Google Cloud network problems using traffic logs, performance metrics, and connection path tests. Identify blocked traffic, packet loss, and latency, summarize traffic volumes, or estimate VPC Flow Logs costs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate blocked traffic and packet loss
• Check latency and throughput trends
• Analyze Cloud NAT port exhaustion

Plan software projects with guidance on short development cycles called sprints, effort estimates, risk management, and stakeholder updates. Clarify responsibilities, coordinate dependencies, and set realistic goals based on the team's capacity.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning sprints around team capacity
• Organizing project risks and mitigation plans
• Clarifying responsibilities across teams

Plan projects that use large language models, from checking whether the task suits a model to designing processing stages. Estimate costs, choose single or multiple agents, and organize prototypes, structured outputs, and iteration.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking whether a task is suitable for an LLM
• Designing a pipeline for batch analysis
• Estimating processing costs before development

Review communications research by searching existing libraries and notes before academic databases. Produce a paper comparison table covering methods, results, and limitations, then summarize major approaches, evidence strength, and remaining research gaps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Surveying research on wireless and satellite communications
• Finding related work from saved papers and notes
• Comparing assumptions, methods, and limitations across papers

Guides LLM training with verl using rewards to improve model responses. Covers data preparation, reward functions, GRPO and PPO configuration, distributed GPU training, and troubleshooting memory shortages or unstable training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train math reasoning models with GRPO and answer-based rewards
• Configure PPO training with a separate value-estimation model
• Run large-model training across multiple GPU nodes

Fine-tune vision models on custom images using Hugging Face Jobs cloud GPUs. Covers object detection, image classification, and segmentation—identifying image regions—with dataset validation, hardware selection, cost estimates, monitoring, and saving trained models to the Hub.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train an object detector on custom images
• Fine-tune an image classifier
• Adapt SAM/SAM2 to custom image masks

Train or fine-tune language and vision models on Hugging Face Jobs without a local GPU. Covers dataset checks, hardware and cost planning, training monitoring, Hub storage, and GGUF conversion for local use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fine-tune a language model without a local GPU
• Validate preference data before DPO training
• Estimate training costs and choose GPU hardware

Classify messages, rate content, or estimate probabilities using defined question types. Receive machine-readable answers, with confidence scores for classification and ratings, and apply the same questions to multiple inputs in a batch.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Routing customer inquiries to departments
• Rating support quality against defined criteria
• Classifying sentiment across multiple messages

Split large feature requests or user stories, descriptions of what users need, into smaller deliverable pieces. Eight patterns guide the breakdown, with acceptance criteria and checks that each piece provides value and can be developed and tested independently.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Breaking down work that will not fit in one sprint
• Clarifying backlog items that are too large to estimate
• Separating stories with multiple acceptance scenarios

Generate new small molecules intended to bind a target protein with Boltz. The workflow covers cost estimation, job submission, result downloads, and ranking candidates by binding confidence or a score for improving promising molecules.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generate binding candidates without an existing compound library
• Rank generated molecules by binding confidence
• Review solubility and permeability of promising candidates

Develop Starknet apps with starknet.js v9.x using examples for contract calls, account management, wallet connections, and transaction handling. It also covers transaction simulation, fee estimates, and paymasters, services that sponsor fees or accept alternative tokens.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reading and updating Starknet smart contracts
• Connecting ArgentX or Braavos wallets
• Simulating transactions and estimating fees before submission

Run multi-agent econometric review on estimation code, identification arguments, and research artifacts

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

End-to-end data analysis workflow in R or Python — from exploration through regression to publication-ready tables and figures. Make sure to use this skill whenever the user wants to run any empirical analysis, write analysis code, or produce output from data. Triggers include: "analyze this data", "run a regression", "write R code for this", "write Python code for this", "I have a dataset", "help me with this regression", "run a DiD", "run an RDD", "event study", "IV regression", "fit a model", "produce a table", "make a figure", "explore my data", or any request involving a dataset path or empirical estimation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Execute research implementation plans efficiently while maintaining estimation quality and finishing features

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Econometrics skill for machine learning methods in causal inference. Activates when the user asks about: "causal forest", "generalized random forest", "GRF", "double machine learning", "DML", "debiased machine learning", "LASSO for variable selection", "post-LASSO", "heterogeneous treatment effects", "CATE", "conditional average treatment effect", "BLP analysis", "CLAN analysis", "causal tree", "honest estimation", "因果森林", "双重机器学习", "异质性处理效应", "条件平均处理效应", "LASSO变量选择", "机器学习因果推断", "去偏机器学习"

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill whenever the user wants to conduct an event study, create event study plots, test for parallel trends, implement difference-in-differences designs, or work with any panel data estimation that involves pre/post treatment comparisons. Trigger on phrases like "event study", "parallel trends", "pre-trends", "dynamic treatment effects", "leads and lags", "TWFE", "two-way fixed effects", "staggered adoption", "staggered treatment", "difference-in-differences", "DiD", "Sun and Abraham", "Callaway and Sant'Anna", "de Chaisemartin", "Borusyak", "did_multiplegt", "fixest", "did2s", "bacon decomposition", or any reference to plotting coefficients around a treatment event. Also trigger when the user uploads panel data and wants to estimate treatment effects with variation in treatment timing. All code is in R.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.