#erroranalysis — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #erroranalysis, aggregated by home.social.
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Alright, future engineers!
**Round-off Error:** Error from storing real numbers with finite precision in a computer.
Ex: `1/3` stored as `0.3333333` loses precision.
Pro-Tip: Accumulates! Small errors can become large in long calc chains.
#NumMethods #ErrorAnalysis #STEM #StudyNotes -
Alright, future engineers!
**Error Bound:** Max diff between numerical approx & true value.
Ex: Reveals worst-case inaccuracy of your solution.
Pro-Tip: Don't just solve, know its limits! Crucial for reliability.
#NumMethods #ErrorAnalysis #STEM #StudyNotes -
Alright, future engineers!
**Truncation Error:** Error from approximating infinite math processes with finite ones.
Ex: Using finite terms of a Taylor series.
Pro-Tip: It's *method-dependent*! Smaller step size often helps, but beware round-off error.
#NumMethods #ErrorAnalysis #STEM #StudyNotes -
Alright, future engineers!
**Truncation Error:** Error from stopping an infinite process (like a series) prematurely.
Ex: Using `1+x` for `e^x` instead of `1+x+x^2/2!` introduces truncation error.
Pro-Tip: Smaller step sizes or more terms reduce it!
#ErrorAnalysis #NumMethods #STEM #StudyNotes -
Alright, future engineers!
**Truncation Error:** Error from stopping an infinite process (like a series) prematurely.
Ex: Using `1+x` for `e^x` instead of `1+x+x^2/2!` introduces truncation error.
Pro-Tip: Smaller step sizes or more terms reduce it!
#ErrorAnalysis #NumMethods #STEM #StudyNotes -
Alright, future engineers!
**Round-off Error:** Error from finite precision in computer arithmetic.
Ex: `1/3` stored as `0.333...3` isn't exact.
Pro-Tip: Accumulates! Can dominate when step sizes are tiny, offsetting truncation error gains.
#NumMethods #ErrorAnalysis #STEM #StudyNotes -
Alright, future engineers!
**Truncation Error:** The error from stopping an infinite calculation early (finite approximation).
Ex: `e^x approx 1+x` (first two Taylor terms).
Pro-Tip: Smaller steps reduce it, but can increase *round-off* error. It's a trade-off!
#NumMethods #ErrorAnalysis #STEM #StudyNotes -
**Error Bounds:** A guaranteed range for the true error of an approximation.
Ex: If your method yields `10 +/- 0.01`, then 0.01 is an error bound.
Pro-Tip: Crucial for knowing how reliable *any* numerical solution is!
#NumericalMethods #ErrorAnalysis #STEM #StudyNotes -
**Truncation Error:** Error from approximating an infinite mathematical process (like a series or integral) with a finite one.
Ex: `e^x ≈ 1+x` incurs truncation error.
Pro-Tip: It's systematic! Reduce it by taking more terms or using smaller step sizes.
#ErrorAnalysis #NumericalMethods #STEM #StudyNotes -
Error bounds quantify the max possible error in a numerical approximation. Ex: For Trapezoidal Rule, Error <= `(b-a)h^2/12 * max|f''(x)|`. Pro-Tip: Essential for knowing if your approximation is good enough for design specs! #NumericalMethods #ErrorAnalysis #STEM #StudyNotes
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Error bounds quantify the max possible error in a numerical approximation. Ex: For Trapezoidal Rule, Error <= `(b-a)h^2/12 * max|f''(x)|`. Pro-Tip: Essential for knowing if your approximation is good enough for design specs! #NumericalMethods #ErrorAnalysis #STEM #StudyNotes
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Alright, future engineers!
**Rounding Error:** Error from representing exact numbers with finite digits.
Ex: 1/3 is 0.333... but stored as 0.3333.
Pro-Tip: It accumulates! Mind precision in long calculations, esp. with huge/tiny numbers.
#NumericalMethods #ErrorAnalysis #STEM #StudyNotes -
Alright, future engineers!
**Rounding Error:** Error from representing exact numbers with finite digits.
Ex: 1/3 is 0.333... but stored as 0.3333.
Pro-Tip: It accumulates! Mind precision in long calculations, esp. with huge/tiny numbers.
#NumericalMethods #ErrorAnalysis #STEM #StudyNotes -
Alright, future engineers!
**Round-off Error:** Error from finite precision number representation in computers.
Ex: 1/3 stored as 0.33333333 has round-off error.
Pro-Tip: Accumulates significantly with many computations, so be aware!
#NumericalMethods #ErrorAnalysis #STEM #StudyNotes -
Alright, future engineers!
**Round-off Error:** Error from finite precision number representation in computers.
Ex: 1/3 stored as 0.33333333 has round-off error.
Pro-Tip: Accumulates significantly with many computations, so be aware!
#NumericalMethods #ErrorAnalysis #STEM #StudyNotes -
Alright, future engineers!
**Truncation Error:** The error from stopping an infinite process (like a series or iteration) after a finite number of steps.
Ex: Approximating `e^x` with only a few terms of its Taylor series.
Pro-Tip: More terms/steps reduce truncation error, but beware of round-off error increasing!
#NumericalMethods #ErrorAnalysis #STEM #StudyNotes -
Alright, future engineers!
**Truncation Error:** The error from stopping an infinite process (like a series or iteration) after a finite number of steps.
Ex: Approximating `e^x` with only a few terms of its Taylor series.
Pro-Tip: More terms/steps reduce truncation error, but beware of round-off error increasing!
#NumericalMethods #ErrorAnalysis #STEM #StudyNotes -
Alright, future engineers!
**Truncation Error** occurs when an exact mathematical procedure is replaced by an approximation, often by cutting off an infinite series.
Ex: Using only the first few terms of a Taylor series for `e^x`.
Pro-Tip: This error is *predictable* and *controllable* by refining your approximation!
#NumericalMethods #ErrorAnalysis #STEM #StudyNotes -
Alright, future engineers!
**Truncation Error** occurs when an exact mathematical procedure is replaced by an approximation, often by cutting off an infinite series.
Ex: Using only the first few terms of a Taylor series for `e^x`.
Pro-Tip: This error is *predictable* and *controllable* by refining your approximation!
#NumericalMethods #ErrorAnalysis #STEM #StudyNotes -
Alright, future engineers!
**Relative Error:** Quantifies how much an approximation deviates, *relative to the true value*. Ex: `RE = |(Approx - True) / True|`. Pro-Tip: Essential for evaluating precision and setting engineering tolerances!
#NumericalMethods #ErrorAnalysis #STEM #StudyNotes -
Alright, future engineers!
**Relative Error:** Quantifies how much an approximation deviates, *relative to the true value*. Ex: `RE = |(Approx - True) / True|`. Pro-Tip: Essential for evaluating precision and setting engineering tolerances!
#NumericalMethods #ErrorAnalysis #STEM #StudyNotes -
Alright, future engineers!
**Truncation Error** is the inaccuracy from ending an infinite math process (like a series or integral) at a finite step. Ex: `e^x` approximated by `1+x`. Pro-Tip: You can *control* it by adjusting step size or terms, unlike round-off error!
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Alright, future engineers!
**Truncation Error** is the inaccuracy from ending an infinite math process (like a series or integral) at a finite step. Ex: `e^x` approximated by `1+x`. Pro-Tip: You can *control* it by adjusting step size or terms, unlike round-off error!
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**Truncation Error** is the error from approximating an infinite mathematical process with a finite one. Ex: Using a finite Taylor series sum. Pro-Tip: It typically decreases with smaller step sizes (h) or more terms (N)!
#NumericalMethods #ErrorAnalysis #STEM #StudyNotes -
**Truncation Error** is the error from approximating an infinite mathematical process with a finite one. Ex: Using a finite Taylor series sum. Pro-Tip: It typically decreases with smaller step sizes (h) or more terms (N)!
#NumericalMethods #ErrorAnalysis #STEM #StudyNotes -
Condition Number measures how sensitive a problem's output is to tiny input changes. Large condition number = huge output errors! Pro-Tip: It's key for understanding solution reliability; high values mean your problem is ill-conditioned and results are unreliable!
#NumericalMethods #ErrorAnalysis #STEM #StudyNotes -
Condition Number measures how sensitive a problem's output is to tiny input changes. Large condition number = huge output errors! Pro-Tip: It's key for understanding solution reliability; high values mean your problem is ill-conditioned and results are unreliable!
#NumericalMethods #ErrorAnalysis #STEM #StudyNotes -
🌐✨ Wow, riveting tech journalism here! Dive deep into... error messages? 🛑🕵️♂️ Apparently, Qualcomm's cutting-edge #GPU requires cutting-edge #patience and JavaScript! 😂🔧
https://chipsandcheese.com/p/diving-into-qualcomms-upcoming-adreno #techjournalism #erroranalysis #Qualcomm #JavaScript #HackerNews #ngated -
🌐✨ Wow, riveting tech journalism here! Dive deep into... error messages? 🛑🕵️♂️ Apparently, Qualcomm's cutting-edge #GPU requires cutting-edge #patience and JavaScript! 😂🔧
https://chipsandcheese.com/p/diving-into-qualcomms-upcoming-adreno #techjournalism #erroranalysis #Qualcomm #JavaScript #HackerNews #ngated -
We look forward to reading your unpublished scholarship discussing: #WorkInProgress, #ExploratoryResearch, #NewProjects, #NegativeResults / #ErrorAnalysis, or #ToolDemos combined with a scholarly argument.
Submission deadline: May 15, 2025 (AoE)
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We look forward to reading your unpublished scholarship discussing: #WorkInProgress, #ExploratoryResearch, #NewProjects, #NegativeResults / #ErrorAnalysis, or #ToolDemos combined with a scholarly argument.
Submission deadline: May 15, 2025 (AoE)
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Trump's Tariff Formula Makes No Economic Sense. It's Also Based on an Error
#HackerNews #TrumpTariffs #EconomicSense #TradePolicy #ErrorAnalysis
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Trump's Tariff Formula Makes No Economic Sense. It's Also Based on an Error
#HackerNews #TrumpTariffs #EconomicSense #TradePolicy #ErrorAnalysis
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The approach they used for #classifying the poems' function in the narrative were interesting, using #LLMs. Performance is pretty bad so far. Keli took up the call for #openness about #failure from a session this morning and showed that the different models are bad in different ways, which allowed the team (and us) to learn something about the models. I think that's great and valuable!
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The approach they used for #classifying the poems' function in the narrative were interesting, using #LLMs. Performance is pretty bad so far. Keli took up the call for #openness about #failure from a session this morning and showed that the different models are bad in different ways, which allowed the team (and us) to learn something about the models. I think that's great and valuable!
