cumulative distribution function of the weibull Search Results


cdf matlab 2023b  (MathWorks Inc)
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MathWorks Inc cdf matlab 2023b
Cdf Matlab 2023b, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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two-parameter weibull cumulative distribution  (OriginLab corp)
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OriginLab corp two-parameter weibull cumulative distribution
The <t>Weibull</t> modulus (m) was the upward gradient of the line. The characteristic strength (σ 0 ) was the strength at a failure probability of approximately 63.2%.
Two Parameter Weibull Cumulative Distribution, supplied by OriginLab corp, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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cumulative weibull function  (Mayrhofer Pharmazeutika)
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Mayrhofer Pharmazeutika cumulative weibull function
Individual animal task acquisition through consecutive shaping stages. A, Learning curves for sound localization without distractor. Rat1 and rat2 were trained on pure tones (8 kHz tone sequence; blue marker), whereas rats 9–12 were trained on white noise (same duration and amplitude parameters; black marker) and transitioned to pure tones once stable performance was reached. A <t>cumulative</t> <t>Weibull</t> function, fitted to the mean performance of consecutive behavioral sessions, shows each animal's dynamic learning phase (shaded green). B, Stable localization performance for white noise (wn), 4, 8, and 16 kHz (the pure tones used in the final spectra-spatial discrimination task). Mean performance and 95% binomial CIs are shown. C, Individual performance for rats 9–12 during the transition from the localization paradigm (no distractor) to the final target-distractor discrimination paradigm. Mean session performance is shown for 16 kHz localization without distractor (circles), with distractor at a 10–20 dB lower amplitude (squares), and finally with distractor amplitude-matched to the target (crosses; 60 dB SPL). Performance falls only when the full-volume distractor is introduced and quickly recovers. Behavioral sessions with the 8 kHz target were interspersed in the training (data not shown for clarity). Rat1 and rat2 were transitioned to the discrimination paradigm using a different pure tone frequency (24 kHz), thus data are not shown.
Cumulative Weibull Function, supplied by Mayrhofer Pharmazeutika, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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weibull model  (Boekel Scientific)
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Boekel Scientific weibull model
Individual animal task acquisition through consecutive shaping stages. A, Learning curves for sound localization without distractor. Rat1 and rat2 were trained on pure tones (8 kHz tone sequence; blue marker), whereas rats 9–12 were trained on white noise (same duration and amplitude parameters; black marker) and transitioned to pure tones once stable performance was reached. A <t>cumulative</t> <t>Weibull</t> function, fitted to the mean performance of consecutive behavioral sessions, shows each animal's dynamic learning phase (shaded green). B, Stable localization performance for white noise (wn), 4, 8, and 16 kHz (the pure tones used in the final spectra-spatial discrimination task). Mean performance and 95% binomial CIs are shown. C, Individual performance for rats 9–12 during the transition from the localization paradigm (no distractor) to the final target-distractor discrimination paradigm. Mean session performance is shown for 16 kHz localization without distractor (circles), with distractor at a 10–20 dB lower amplitude (squares), and finally with distractor amplitude-matched to the target (crosses; 60 dB SPL). Performance falls only when the full-volume distractor is introduced and quickly recovers. Behavioral sessions with the 8 kHz target were interspersed in the training (data not shown for clarity). Rat1 and rat2 were transitioned to the discrimination paradigm using a different pure tone frequency (24 kHz), thus data are not shown.
Weibull Model, supplied by Boekel Scientific, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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psychometric curve using the cumulative weibull distribution function  (MathWorks Inc)
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MathWorks Inc psychometric curve using the cumulative weibull distribution function
Individual animal task acquisition through consecutive shaping stages. A, Learning curves for sound localization without distractor. Rat1 and rat2 were trained on pure tones (8 kHz tone sequence; blue marker), whereas rats 9–12 were trained on white noise (same duration and amplitude parameters; black marker) and transitioned to pure tones once stable performance was reached. A <t>cumulative</t> <t>Weibull</t> function, fitted to the mean performance of consecutive behavioral sessions, shows each animal's dynamic learning phase (shaded green). B, Stable localization performance for white noise (wn), 4, 8, and 16 kHz (the pure tones used in the final spectra-spatial discrimination task). Mean performance and 95% binomial CIs are shown. C, Individual performance for rats 9–12 during the transition from the localization paradigm (no distractor) to the final target-distractor discrimination paradigm. Mean session performance is shown for 16 kHz localization without distractor (circles), with distractor at a 10–20 dB lower amplitude (squares), and finally with distractor amplitude-matched to the target (crosses; 60 dB SPL). Performance falls only when the full-volume distractor is introduced and quickly recovers. Behavioral sessions with the 8 kHz target were interspersed in the training (data not shown for clarity). Rat1 and rat2 were transitioned to the discrimination paradigm using a different pure tone frequency (24 kHz), thus data are not shown.
Psychometric Curve Using The Cumulative Weibull Distribution Function, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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weibull cumulative distribution function  (MathWorks Inc)
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MathWorks Inc weibull cumulative distribution function
Individual animal task acquisition through consecutive shaping stages. A, Learning curves for sound localization without distractor. Rat1 and rat2 were trained on pure tones (8 kHz tone sequence; blue marker), whereas rats 9–12 were trained on white noise (same duration and amplitude parameters; black marker) and transitioned to pure tones once stable performance was reached. A <t>cumulative</t> <t>Weibull</t> function, fitted to the mean performance of consecutive behavioral sessions, shows each animal's dynamic learning phase (shaded green). B, Stable localization performance for white noise (wn), 4, 8, and 16 kHz (the pure tones used in the final spectra-spatial discrimination task). Mean performance and 95% binomial CIs are shown. C, Individual performance for rats 9–12 during the transition from the localization paradigm (no distractor) to the final target-distractor discrimination paradigm. Mean session performance is shown for 16 kHz localization without distractor (circles), with distractor at a 10–20 dB lower amplitude (squares), and finally with distractor amplitude-matched to the target (crosses; 60 dB SPL). Performance falls only when the full-volume distractor is introduced and quickly recovers. Behavioral sessions with the 8 kHz target were interspersed in the training (data not shown for clarity). Rat1 and rat2 were transitioned to the discrimination paradigm using a different pure tone frequency (24 kHz), thus data are not shown.
Weibull Cumulative Distribution Function, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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nist/sematech e-handbook statistical methods  (SEMATECH Inc)
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SEMATECH Inc nist/sematech e-handbook statistical methods
Individual animal task acquisition through consecutive shaping stages. A, Learning curves for sound localization without distractor. Rat1 and rat2 were trained on pure tones (8 kHz tone sequence; blue marker), whereas rats 9–12 were trained on white noise (same duration and amplitude parameters; black marker) and transitioned to pure tones once stable performance was reached. A <t>cumulative</t> <t>Weibull</t> function, fitted to the mean performance of consecutive behavioral sessions, shows each animal's dynamic learning phase (shaded green). B, Stable localization performance for white noise (wn), 4, 8, and 16 kHz (the pure tones used in the final spectra-spatial discrimination task). Mean performance and 95% binomial CIs are shown. C, Individual performance for rats 9–12 during the transition from the localization paradigm (no distractor) to the final target-distractor discrimination paradigm. Mean session performance is shown for 16 kHz localization without distractor (circles), with distractor at a 10–20 dB lower amplitude (squares), and finally with distractor amplitude-matched to the target (crosses; 60 dB SPL). Performance falls only when the full-volume distractor is introduced and quickly recovers. Behavioral sessions with the 8 kHz target were interspersed in the training (data not shown for clarity). Rat1 and rat2 were transitioned to the discrimination paradigm using a different pure tone frequency (24 kHz), thus data are not shown.
Nist/Sematech E Handbook Statistical Methods, supplied by SEMATECH Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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weibull cumulative damage analysis alta-pro  (Reliasoft Corporation)
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Reliasoft Corporation weibull cumulative damage analysis alta-pro
Individual animal task acquisition through consecutive shaping stages. A, Learning curves for sound localization without distractor. Rat1 and rat2 were trained on pure tones (8 kHz tone sequence; blue marker), whereas rats 9–12 were trained on white noise (same duration and amplitude parameters; black marker) and transitioned to pure tones once stable performance was reached. A <t>cumulative</t> <t>Weibull</t> function, fitted to the mean performance of consecutive behavioral sessions, shows each animal's dynamic learning phase (shaded green). B, Stable localization performance for white noise (wn), 4, 8, and 16 kHz (the pure tones used in the final spectra-spatial discrimination task). Mean performance and 95% binomial CIs are shown. C, Individual performance for rats 9–12 during the transition from the localization paradigm (no distractor) to the final target-distractor discrimination paradigm. Mean session performance is shown for 16 kHz localization without distractor (circles), with distractor at a 10–20 dB lower amplitude (squares), and finally with distractor amplitude-matched to the target (crosses; 60 dB SPL). Performance falls only when the full-volume distractor is introduced and quickly recovers. Behavioral sessions with the 8 kHz target were interspersed in the training (data not shown for clarity). Rat1 and rat2 were transitioned to the discrimination paradigm using a different pure tone frequency (24 kHz), thus data are not shown.
Weibull Cumulative Damage Analysis Alta Pro, supplied by Reliasoft Corporation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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inverse power law-weibull cumulative damage model alta pro  (Reliasoft Corporation)
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Reliasoft Corporation inverse power law-weibull cumulative damage model alta pro
Individual animal task acquisition through consecutive shaping stages. A, Learning curves for sound localization without distractor. Rat1 and rat2 were trained on pure tones (8 kHz tone sequence; blue marker), whereas rats 9–12 were trained on white noise (same duration and amplitude parameters; black marker) and transitioned to pure tones once stable performance was reached. A <t>cumulative</t> <t>Weibull</t> function, fitted to the mean performance of consecutive behavioral sessions, shows each animal's dynamic learning phase (shaded green). B, Stable localization performance for white noise (wn), 4, 8, and 16 kHz (the pure tones used in the final spectra-spatial discrimination task). Mean performance and 95% binomial CIs are shown. C, Individual performance for rats 9–12 during the transition from the localization paradigm (no distractor) to the final target-distractor discrimination paradigm. Mean session performance is shown for 16 kHz localization without distractor (circles), with distractor at a 10–20 dB lower amplitude (squares), and finally with distractor amplitude-matched to the target (crosses; 60 dB SPL). Performance falls only when the full-volume distractor is introduced and quickly recovers. Behavioral sessions with the 8 kHz target were interspersed in the training (data not shown for clarity). Rat1 and rat2 were transitioned to the discrimination paradigm using a different pure tone frequency (24 kHz), thus data are not shown.
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Image Search Results


The Weibull modulus (m) was the upward gradient of the line. The characteristic strength (σ 0 ) was the strength at a failure probability of approximately 63.2%.

Journal: PeerJ

Article Title: Effect of thermal cycling on the mechanical properties of conventional, milled, and 3D-printed base resin materials: a comparative in vitro study

doi: 10.7717/peerj.19141

Figure Lengend Snippet: The Weibull modulus (m) was the upward gradient of the line. The characteristic strength (σ 0 ) was the strength at a failure probability of approximately 63.2%.

Article Snippet: Additionally, a two-parameter Weibull cumulative distribution was conducted on the flexural strength and impact strength (Origin(Pro), Version 2024; OriginLab Corporation, Northampton, MA, USA.) to calculate the Weibull modulus and Scale parameter.

Techniques:

The Weibull modulus (m) was the upward gradient of the line. The characteristic strength (σ 0 ) was the strength at a failure probability of approximately 63.2%.

Journal: PeerJ

Article Title: Effect of thermal cycling on the mechanical properties of conventional, milled, and 3D-printed base resin materials: a comparative in vitro study

doi: 10.7717/peerj.19141

Figure Lengend Snippet: The Weibull modulus (m) was the upward gradient of the line. The characteristic strength (σ 0 ) was the strength at a failure probability of approximately 63.2%.

Article Snippet: Additionally, a two-parameter Weibull cumulative distribution was conducted on the flexural strength and impact strength (Origin(Pro), Version 2024; OriginLab Corporation, Northampton, MA, USA.) to calculate the Weibull modulus and Scale parameter.

Techniques:

Individual animal task acquisition through consecutive shaping stages. A, Learning curves for sound localization without distractor. Rat1 and rat2 were trained on pure tones (8 kHz tone sequence; blue marker), whereas rats 9–12 were trained on white noise (same duration and amplitude parameters; black marker) and transitioned to pure tones once stable performance was reached. A cumulative Weibull function, fitted to the mean performance of consecutive behavioral sessions, shows each animal's dynamic learning phase (shaded green). B, Stable localization performance for white noise (wn), 4, 8, and 16 kHz (the pure tones used in the final spectra-spatial discrimination task). Mean performance and 95% binomial CIs are shown. C, Individual performance for rats 9–12 during the transition from the localization paradigm (no distractor) to the final target-distractor discrimination paradigm. Mean session performance is shown for 16 kHz localization without distractor (circles), with distractor at a 10–20 dB lower amplitude (squares), and finally with distractor amplitude-matched to the target (crosses; 60 dB SPL). Performance falls only when the full-volume distractor is introduced and quickly recovers. Behavioral sessions with the 8 kHz target were interspersed in the training (data not shown for clarity). Rat1 and rat2 were transitioned to the discrimination paradigm using a different pure tone frequency (24 kHz), thus data are not shown.

Journal: The Journal of Neuroscience

Article Title: Evoked Response Strength in Primary Auditory Cortex Predicts Performance in a Spectro-Spatial Discrimination Task in Rats

doi: 10.1523/JNEUROSCI.0041-18.2019

Figure Lengend Snippet: Individual animal task acquisition through consecutive shaping stages. A, Learning curves for sound localization without distractor. Rat1 and rat2 were trained on pure tones (8 kHz tone sequence; blue marker), whereas rats 9–12 were trained on white noise (same duration and amplitude parameters; black marker) and transitioned to pure tones once stable performance was reached. A cumulative Weibull function, fitted to the mean performance of consecutive behavioral sessions, shows each animal's dynamic learning phase (shaded green). B, Stable localization performance for white noise (wn), 4, 8, and 16 kHz (the pure tones used in the final spectra-spatial discrimination task). Mean performance and 95% binomial CIs are shown. C, Individual performance for rats 9–12 during the transition from the localization paradigm (no distractor) to the final target-distractor discrimination paradigm. Mean session performance is shown for 16 kHz localization without distractor (circles), with distractor at a 10–20 dB lower amplitude (squares), and finally with distractor amplitude-matched to the target (crosses; 60 dB SPL). Performance falls only when the full-volume distractor is introduced and quickly recovers. Behavioral sessions with the 8 kHz target were interspersed in the training (data not shown for clarity). Rat1 and rat2 were transitioned to the discrimination paradigm using a different pure tone frequency (24 kHz), thus data are not shown.

Article Snippet: A cumulative Weibull function was fitted to performance over consecutive sessions of the localization paradigm (no distractor) to produce learning curves ( A ), with the dynamic learning phase defined as the range between the first and ninth decile of the function, as previously described by Mayrhofer et al. (2013) .

Techniques: Sequencing, Marker