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a/_images/8b9e918cc19e7e08dbde93f0cdb32797cd584ce1aedfa3745935c396b572ae05.png b/_images/8b9e918cc19e7e08dbde93f0cdb32797cd584ce1aedfa3745935c396b572ae05.png new file mode 100644 index 000000000..a093b431e Binary files /dev/null and b/_images/8b9e918cc19e7e08dbde93f0cdb32797cd584ce1aedfa3745935c396b572ae05.png differ diff --git a/_images/67b20ab3b147296d9d5663fcf8d4dcaad719a70207873a4b14ec30fbec980997.svg b/_images/8f087e601037e25b07379dd8e1b8d993fcd59812c73fec2795c3cf36b2d9cbfe.svg similarity index 100% rename from _images/67b20ab3b147296d9d5663fcf8d4dcaad719a70207873a4b14ec30fbec980997.svg rename to _images/8f087e601037e25b07379dd8e1b8d993fcd59812c73fec2795c3cf36b2d9cbfe.svg index 3fc302f1e..b85a91104 100644 --- a/_images/67b20ab3b147296d9d5663fcf8d4dcaad719a70207873a4b14ec30fbec980997.svg +++ b/_images/8f087e601037e25b07379dd8e1b8d993fcd59812c73fec2795c3cf36b2d9cbfe.svg @@ -36,16 +36,8 @@ - - -hgf_loglike - -hgf_loglike -~ -Potential - - + mu_omega_1 mu_omega_1 @@ -58,6 +50,14 @@ + + +hgf_loglike + +hgf_loglike +~ +Potential + offset diff --git a/_images/8f02834ccc45a1fbb4bd4aabfac2d75a89a86b3ef90438ded772ac7bf43de688.svg b/_images/8fc9c210ab62f5dd9a3be3ce533d3c8c7ee05351b66a0f94da4f8a26be10ae39.svg similarity index 100% rename from _images/8f02834ccc45a1fbb4bd4aabfac2d75a89a86b3ef90438ded772ac7bf43de688.svg rename to _images/8fc9c210ab62f5dd9a3be3ce533d3c8c7ee05351b66a0f94da4f8a26be10ae39.svg index 0255e9b57..a85f1c417 100644 --- a/_images/8f02834ccc45a1fbb4bd4aabfac2d75a89a86b3ef90438ded772ac7bf43de688.svg +++ b/_images/8fc9c210ab62f5dd9a3be3ce533d3c8c7ee05351b66a0f94da4f8a26be10ae39.svg @@ -9,22 +9,22 @@ %3 - - -hgf_loglike - -hgf_loglike -~ -Potential - - + tonic_volatility_2 tonic_volatility_2 ~ Normal + + +hgf_loglike + +hgf_loglike +~ +Potential + tonic_volatility_2->hgf_loglike diff --git a/_images/9d2137dac5b63c3918f1605c8f8886a83ec6a30ce2220d8e3b876a0574b5759f.png b/_images/9d2137dac5b63c3918f1605c8f8886a83ec6a30ce2220d8e3b876a0574b5759f.png new file 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rename from _images/fdbabab328fc5335523b02022da2d71f6b6a52d224f769f8a8295f4cff4d8579.svg rename to _images/c9d3add6fa19bee19fc6b310e1cb563b33fb5ddcde9daa677cf58fbf34add0af.svg index cb442273e..ef37863bd 100644 --- a/_images/fdbabab328fc5335523b02022da2d71f6b6a52d224f769f8a8295f4cff4d8579.svg +++ b/_images/c9d3add6fa19bee19fc6b310e1cb563b33fb5ddcde9daa677cf58fbf34add0af.svg @@ -23,7 +23,7 @@ HalfCauchy - + tonic_volatility_1 tonic_volatility_1 @@ -36,16 +36,8 @@ - - -hgf_loglike - -hgf_loglike -~ -Potential - - + mu_omega_1 mu_omega_1 @@ -58,14 +50,16 @@ - - -tonic_volatility_1->hgf_loglike - - + + +hgf_loglike + +hgf_loglike +~ +Potential - + offset offset @@ -78,5 +72,11 @@ + + +tonic_volatility_1->hgf_loglike + + + \ No newline at end of file diff --git a/_images/cbc4c5e9ec777751983e709052ec0889618593fd8d4c76c7921b7b3b264266c9.png b/_images/cbc4c5e9ec777751983e709052ec0889618593fd8d4c76c7921b7b3b264266c9.png deleted file mode 100644 index ba7dac095..000000000 Binary files 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b/_images/ecd192b619fcda3b40cdb5f737d2b936691486677eb7a4f82b5cbc7f55367e9e.svg @@ -9,22 +9,22 @@ %3 - - -tonic_volatility_2 - -tonic_volatility_2 -~ -Uniform - - + hgf_loglike hgf_loglike ~ Potential + + +tonic_volatility_2 + +tonic_volatility_2 +~ +Uniform + tonic_volatility_2->hgf_loglike diff --git a/_images/ef445b5991a02c579c9aba554328d03ac4e57f7d39f4e5e5c5f23f1f3f5a2cbd.png b/_images/ef445b5991a02c579c9aba554328d03ac4e57f7d39f4e5e5c5f23f1f3f5a2cbd.png new file mode 100644 index 000000000..c6d79e444 Binary files /dev/null and b/_images/ef445b5991a02c579c9aba554328d03ac4e57f7d39f4e5e5c5f23f1f3f5a2cbd.png differ diff --git a/_images/f3cb22160d36acee6c8de63e7eea9fd921183335221f0469c72418b2838e218d.png b/_images/f3cb22160d36acee6c8de63e7eea9fd921183335221f0469c72418b2838e218d.png deleted file mode 100644 index 0cbb8fc91..000000000 Binary files a/_images/f3cb22160d36acee6c8de63e7eea9fd921183335221f0469c72418b2838e218d.png and /dev/null differ diff --git a/_images/fd39779b623d37b151ad52dbe1c8f1b88d3a9166f7c82a25fe3f219f76fef694.png b/_images/fd39779b623d37b151ad52dbe1c8f1b88d3a9166f7c82a25fe3f219f76fef694.png new file mode 100644 index 000000000..f800309dd Binary files /dev/null and b/_images/fd39779b623d37b151ad52dbe1c8f1b88d3a9166f7c82a25fe3f219f76fef694.png differ diff --git a/_images/fdbf1b50a4529829b77d4f8c3d8d55a816316cf36fae0d43d90bb631df267d20.png b/_images/fdbf1b50a4529829b77d4f8c3d8d55a816316cf36fae0d43d90bb631df267d20.png new file mode 100644 index 000000000..fcc4d5a7d Binary files /dev/null and b/_images/fdbf1b50a4529829b77d4f8c3d8d55a816316cf36fae0d43d90bb631df267d20.png differ diff --git a/_modules/pyhgf/model.html b/_modules/pyhgf/model.html index 2bce7827a..146febdf3 100644 --- a/_modules/pyhgf/model.html +++ b/_modules/pyhgf/model.html @@ -480,17 +480,6 @@

Source code for pyhgf.model

     ----------
     attributes :
         The attributes of the probabilistic nodes.
-    allow_missing_inputs :
-        If `False` (default), all the observation provided to the input nodes should be
-        real or integer. If `True`, missing inputs are allowed as `jnp.nan`. In case of
-        missing inputs, the continuous parents are updated by decreasing the precision
-        as a function of time. Allowing for missing inputs add a conditional check for
-        `jnp.nan` at every time step and should therefore be avoided if the input time
-        series is certified without missing inputs.
-    .. warning::
-        Missing inputs are missing observation from the agent perspective and should
-        not be used to handle missing data point that are only missing in the event
-        log, or rejected trials.
     edges :
         The edges of the probabilistic nodes as a tuple of
         :py:class:`pyhgf.typing.Indexes`. The tuple has the same length as node number.
@@ -561,7 +550,6 @@ 
Source code for pyhgf.model
             "3": 0.0,
         },
         verbose: bool = True,
-        allow_missing_inputs: bool = False,
     ):
         r"""Parameterization of the HGF model.
 
@@ -613,8 +601,6 @@ 
Source code for pyhgf.model
         verbose :
             The verbosity of the methods for model creation and fitting. Defaults to
             `True`.
-        allow_missing_inputs :
-            Whether the network should handle missing inputs. Defaults to `False`.
 
         """
         self.model_type = model_type
@@ -626,7 +612,6 @@ 
Source code for pyhgf.model
         self.attributes: Dict = {}
         self.update_sequence: Optional[UpdateSequence] = None
         self.scan_fn: Optional[Callable] = None
-        self.allow_missing_inputs = allow_missing_inputs
 
         if model_type not in ["continuous", "binary"]:
             if self.verbose:
@@ -771,7 +756,17 @@ 
Source code for pyhgf.model
             `np.ones(len(input_data))`. This vector is automatically transformed
             into a time steps vector.
         is_observed :
-            A 2d boolean array masking `input_data`.
+            A 2d boolean array masking `input_data`. In case of missing inputs, (i.e.
+            `is_observed` is `0`), the input node will have value and volatility set to
+            `0.0`. If the parent(s) of this input receive prediction error from other
+            children, they simply ignore this one. If they are not receiving other
+            prediction errors, they are updated by keeping the same mean be decreasing
+            the precision as a function of time to reflect the evolution of the
+            underlying Gaussian Random Walk.
+        .. warning::
+            Missing inputs are missing observations from the agent's perspective and
+            should not be used to handle missing data points that were observed (e.g.
+            missing in the event log, or rejected trials).
 
         """
         if self.verbose:
@@ -832,7 +827,17 @@ 
Source code for pyhgf.model
             `np.ones(len(input_data))`. This vector is automatically transformed
             into a time steps vector.
         is_observed :
-            A 2d boolean array masking `input_data`.
+            A 2d boolean array masking `input_data`. In case of missing inputs, (i.e.
+            `is_observed` is `0`), the input node will have value and volatility set to
+            `0.0`. If the parent(s) of this input receive prediction error from other
+            children, they simply ignore this one. If they are not receiving other
+            prediction errors, they are updated by keeping the same mean be decreasing
+            the precision as a function of time to reflect the evolution of the
+            underlying Gaussian Random Walk.
+        .. warning::
+            Missing inputs are missing observations from the agent's perspective and
+            should not be used to handle missing data points that were observed (e.g.
+            missing in the event log, or rejected trials).
 
         """
         if self.verbose:
diff --git a/_modules/pyhgf/networks.html b/_modules/pyhgf/networks.html
index d6b8bbb8c..5930c5b8b 100644
--- a/_modules/pyhgf/networks.html
+++ b/_modules/pyhgf/networks.html
@@ -464,7 +464,6 @@ 
Source code for pyhgf.networks
 from pyhgf.updates.posterior.continuous import (
     continuous_node_update,
     continuous_node_update_ehgf,
-    continuous_node_update_missing_observations,
 )
 from pyhgf.updates.prediction.binary import binary_state_node_prediction
 from pyhgf.updates.prediction.continuous import continuous_node_prediction
@@ -778,22 +777,6 @@ 
Source code for pyhgf.networks
             update_fn = continuous_input_prediction_error
             update_sequence.append((input_idx, update_fn))
 
-            # special case: the network should handle missing inputs
-            # the continuous parent of the input node node is also updated here
-            if hgf.allow_missing_inputs:
-                continuous_parent_idx = hgf.edges[input_idx].value_parents[
-                    0
-                ]  # type: ignore
-                update_sequence.append(
-                    (continuous_parent_idx, continuous_node_update_missing_observations)
-                )
-                node_without_update.remove(continuous_parent_idx)
-
-                # the prediction sequence is the update sequence in reverse order
-                prediction_sequence.insert(
-                    0, (continuous_parent_idx, continuous_node_prediction)
-                )
-
         elif kind == "binary":
             # add the update steps for the binary state node as well
             binary_state_idx = hgf.edges[input_idx].value_parents[0]  # type: ignore
@@ -819,22 +802,6 @@ 
Source code for pyhgf.networks
                 0, (binary_state_idx, binary_state_node_prediction)
             )
 
-            # special case: the network should handle missing inputs
-            # the continuous parent of the binary state node is also updated here
-            if hgf.allow_missing_inputs:
-                continuous_parent_idx = hgf.edges[binary_state_idx].value_parents[
-                    0
-                ]  # type: ignore
-                update_sequence.append(
-                    (continuous_parent_idx, continuous_node_update_missing_observations)
-                )
-                node_without_update.remove(continuous_parent_idx)
-
-                # the prediction sequence is the update sequence in reverse order
-                prediction_sequence.insert(
-                    0, (continuous_parent_idx, continuous_node_prediction)
-                )
-
         # add the PE step to the sequence
         node_without_pe.remove(input_idx)
 
diff --git a/_modules/pyhgf/updates/posterior/continuous.html b/_modules/pyhgf/updates/posterior/continuous.html
index 0d136599a..1fa5b0ed2 100644
--- a/_modules/pyhgf/updates/posterior/continuous.html
+++ b/_modules/pyhgf/updates/posterior/continuous.html
@@ -449,12 +449,10 @@ 
Source code for pyhgf.updates.posterior.continuous
# Author: Nicolas Legrand <nicolas.legrand@cas.au.dk>
 
 from functools import partial
-from typing import Callable, Dict
+from typing import Dict
 
 import jax.numpy as jnp
-from jax import Array, jit
-from jax.lax import cond
-from jax.tree_util import Partial
+from jax import jit
 
 from pyhgf.typing import Edges
 
@@ -959,131 +957,6 @@ 
Source code for pyhgf.updates.posterior.continuous
return attributes

 
-
-
-

-[docs]
-@partial(jit, static_argnames=("edges", "node_idx"))
-def continuous_node_update_missing_observations(
-    attributes: Dict,
-    edges: Edges,
-    time_step: float,
-    node_idx: int,
-    update_fn: Callable = continuous_node_update,
-    **args
-) -> Array:
-    """Update the posterior of a continuous node under missing observations.
-
-    Parameters
-    ----------
-    attributes :
-        The attributes of the probabilistic network that contains the continuous state
-        node.
-    edges :
-        The edges of the probabilistic network as a tuple of
-        :py:class:`pyhgf.typing.Indexes`. The tuple has the same length as the number of
-        nodes. For each node, the index list value/volatility - parents/children.
-    time_step :
-        The time interval between the previous time point and the current time point.
-    node_idx :
-        Index of the node that should be updated.
-    update_fn :
-        The default update function to use if there is at least one observation.
-
-    Returns
-    -------
-    attributes :
-        The attributes of the probabilistic network that contains the continuous state
-        node.
-
-    """
-    # define the two possible branches of updates
-    blank_fn = Partial(
-        continuous_blank_update, node_idx=node_idx, edges=edges, time_step=time_step
-    )
-    regular_fn = Partial(update_fn, node_idx=node_idx, edges=edges)
-
-    # for all children, look at the values of VAPE
-    # if all these values are NaNs, the node has not received observations
-    observations = []
-    if edges[node_idx].value_children is not None:
-        for children_idx in edges[node_idx].value_children:  # type: ignore
-            observations.append(
-                attributes[children_idx]["temp"]["value_prediction_error"]
-            )
-    if edges[node_idx].volatility_children is not None:
-        for children_idx in edges[node_idx].volatility_children:  # type: ignore
-            observations.append(
-                attributes[children_idx]["temp"]["value_prediction_error"]
-            )
-    observations = jnp.sum(jnp.array(observations))
-
-    # if the observation is missing, use the blank update
-    # otherwise use the regular update for binary inputs
-    attributes = cond(jnp.isnan(observations), blank_fn, regular_fn, attributes)
-
-    return attributes

-
-
-
-

-[docs]
-@partial(jit, static_argnames=("edges", "node_idx"))
-def continuous_blank_update(
-    attributes: Dict, time_step: float, edges: Edges, node_idx: int
-) -> Array:
-    r"""Compute the precision of a continuous state node under missing observations.
-
-    Parameters
-    ----------
-    attributes :
-        The attributes of the probabilistic network that contains the continuous state
-        node.
-    edges :
-        The edges of the probabilistic network as a tuple of
-        :py:class:`pyhgf.typing.Indexes`. The tuple has the same length as the number of
-        nodes. For each node, the index list value/volatility - parents/children.
-    time_step :
-        The time interval between the previous time point and the current time point.
-    node_idx :
-        Index of the node that should be updated.
-
-    Returns
-    -------
-    precision :
-        The new expected precision of the value parent.
-
-    """
-    # List the node's volatility parents
-    volatility_parents_idxs = edges[node_idx].volatility_parents
-
-    # Get the tonic volatility from the node
-    total_volatility = attributes[node_idx]["tonic_volatility"]
-
-    # Look at the (optional) volatility parents and add their value to the tonic
-    # volatility to get the total volatility
-    if volatility_parents_idxs is not None:
-        for volatility_parents_idx, volatility_coupling in zip(
-            volatility_parents_idxs,
-            attributes[node_idx]["volatility_coupling_parents"],
-        ):
-            total_volatility += (
-                volatility_coupling * attributes[volatility_parents_idx]["mean"]
-            )
-
-    # compute the predicted_volatility from the total volatility
-    predicted_volatility = time_step * jnp.exp(total_volatility)
-    predicted_volatility = jnp.where(
-        predicted_volatility > 1e-128, predicted_volatility, jnp.nan
-    )
-
-    # Estimate the new precision for the continuous state node
-    precision = 1 / ((1 / attributes[node_idx]["precision"]) + predicted_volatility)
-
-    attributes[node_idx]["precision"] = precision
-
-    return attributes

-
 

 
                 
diff --git a/_sources/api.rst.txt b/_sources/api.rst.txt
index 2896f09a7..217bfd90a 100644
--- a/_sources/api.rst.txt
+++ b/_sources/api.rst.txt
@@ -56,8 +56,6 @@ Continuous nodes
     posterior_update_precision_continuous_node
     continuous_node_update
     continuous_node_update_ehgf
-    continuous_node_update_missing_observations
-    continuous_blank_update
 
 Prediction steps
 ================
diff --git a/_sources/generated/pyhgf.updates.posterior.continuous/pyhgf.updates.posterior.continuous.continuous_blank_update.rst.txt b/_sources/generated/pyhgf.updates.posterior.continuous/pyhgf.updates.posterior.continuous.continuous_blank_update.rst.txt
deleted file mode 100644
index e56fa8ecc..000000000
--- a/_sources/generated/pyhgf.updates.posterior.continuous/pyhgf.updates.posterior.continuous.continuous_blank_update.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-pyhgf.updates.posterior.continuous.continuous\_blank\_update
-============================================================
-
-.. currentmodule:: pyhgf.updates.posterior.continuous
-
-.. autofunction:: continuous_blank_update
\ No newline at end of file
diff --git a/_sources/generated/pyhgf.updates.posterior.continuous/pyhgf.updates.posterior.continuous.continuous_node_update_missing_observations.rst.txt b/_sources/generated/pyhgf.updates.posterior.continuous/pyhgf.updates.posterior.continuous.continuous_node_update_missing_observations.rst.txt
deleted file mode 100644
index d1a65d524..000000000
--- a/_sources/generated/pyhgf.updates.posterior.continuous/pyhgf.updates.posterior.continuous.continuous_node_update_missing_observations.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-pyhgf.updates.posterior.continuous.continuous\_node\_update\_missing\_observations
-==================================================================================
-
-.. currentmodule:: pyhgf.updates.posterior.continuous
-
-.. autofunction:: continuous_node_update_missing_observations
\ No newline at end of file
diff --git a/api.html b/api.html
index 1491863f9..16582fba4 100644
--- a/api.html
+++ b/api.html
@@ -545,12 +545,6 @@ 
Continuous nodes<
 
continuous_node_update_ehgf(attributes, ...)

 
Update the posterior of a continuous node using the eHGF update.

 
-
continuous_node_update_missing_observations(...)

-
Update the posterior of a continuous node under missing observations.

-
-
continuous_blank_update(attributes, ...)

-
Compute the precision of a continuous state node under missing observations.

-
 
 
 
diff --git a/generated/pyhgf.distribution/pyhgf.distribution.HGFDistribution.html b/generated/pyhgf.distribution/pyhgf.distribution.HGFDistribution.html
index dc4cbb213..49e81a23a 100644
--- a/generated/pyhgf.distribution/pyhgf.distribution.HGFDistribution.html
+++ b/generated/pyhgf.distribution/pyhgf.distribution.HGFDistribution.html
@@ -417,8 +417,6 @@
 
  • pyhgf.updates.posterior.continuous.posterior_update_precision_continuous_node
    • 
 
  • pyhgf.updates.posterior.continuous.continuous_node_update
    • 
 
  • pyhgf.updates.posterior.continuous.continuous_node_update_ehgf
    • 
-
  • pyhgf.updates.posterior.continuous.continuous_node_update_missing_observations
    • 
-
  • pyhgf.updates.posterior.continuous.continuous_blank_update
    • 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
    ../_images/e5d0619a869ceabba172f4244ed5bdb385502171cfbebe274468cac8e84a4b05.png
+
    ../_images/9d2137dac5b63c3918f1605c8f8886a83ec6a30ce2220d8e3b876a0574b5759f.png
 
    
 
    
 
    The results above indicate that given the responses provided by the participant, the most likely values for the parameter \(\omega_2\) are between -4.9 and -3.1, with a mean at -3.9 (you can find slightly different values if you sample different actions from the decisions function). We can consider this as an excellent estimate given the sparsity of the data, and the complexity of the model.
    
@@ -964,12 +964,12 @@ 
    System configuration
    
diff --git a/notebooks/3-Multilevel_HGF.html b/notebooks/3-Multilevel_HGF.html
index 556ebbbaf..0ca6dbb1f 100644
--- a/notebooks/3-Multilevel_HGF.html
+++ b/notebooks/3-Multilevel_HGF.html
@@ -620,7 +620,7 @@ 
    Model#<
    -../_images/fdbabab328fc5335523b02022da2d71f6b6a52d224f769f8a8295f4cff4d8579.svg
    +../_images/c9d3add6fa19bee19fc6b310e1cb563b33fb5ddcde9daa677cf58fbf34add0af.svg
    @@ -637,7 +637,7 @@

    Model#< 

    WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
-I0000 00:00:1701338299.961452    3247 tfrt_cpu_pjrt_client.cc:349] TfrtCpuClient created.
+I0000 00:00:1701338985.145151    2784 tfrt_cpu_pjrt_client.cc:349] TfrtCpuClient created.
 No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)
    @@ -666,9 +666,9 @@

    Model#<
    - 100.00% [2000/2000 00:58<00:00 Sampling chain 0, 10 divergences] + 100.00% [2000/2000 01:02<00:00 Sampling chain 0, 3 divergences]
    - 
    Sampling 1 chain for 1_000 tune and 1_000 draw iterations (1_000 + 1_000 draws total) took 59 seconds.
+    
    Sampling 1 chain for 1_000 tune and 1_000 draw iterations (1_000 + 1_000 draws total) took 62 seconds.
 
    Only one chain was sampled, this makes it impossible to run some convergence checks
@@ -684,7 +684,7 @@ 
    Model#<
    -../_images/29f51c4d91c9e9b4cc1c30d41e0f6369fd7e14ec768afad7042e81ee13d90a2a.png +../_images/a9b52ae211df5a9109bd3001d28bd640cf347ad2ed6d605b7a3927b913a1ad5e.png

    As expected, the highest density aroud the group-level mean estimate inlcude 10.0, the real value.

    @@ -765,7 +765,7 @@

    Visualizing the model
    -../_images/67b20ab3b147296d9d5663fcf8d4dcaad719a70207873a4b14ec30fbec980997.svg
    +../_images/8f087e601037e25b07379dd8e1b8d993fcd59812c73fec2795c3cf36b2d9cbfe.svg
    @@ -809,9 +809,9 @@

    Sampling
    - 100.00% [2000/2000 00:17<00:00 Sampling chain 0, 26 divergences] + 100.00% [2000/2000 00:22<00:00 Sampling chain 0, 33 divergences]
    - 
    Sampling 1 chain for 1_000 tune and 1_000 draw iterations (1_000 + 1_000 draws total) took 18 seconds.
+    
    Sampling 1 chain for 1_000 tune and 1_000 draw iterations (1_000 + 1_000 draws total) took 23 seconds.
 
    Only one chain was sampled, this makes it impossible to run some convergence checks
@@ -827,7 +827,7 @@ 
    Sampling
    -../_images/2bdb8ea958ef5dccf49f514358725a9bcbf90ca3622ff4c5f17d5a34d520e73c.png +../_images/b9b365f1dee507e4da09bf4c2216ae7763184927453201aebddcbd38c44c3768.png

    @@ -854,11 +854,11 @@

    System configurationEmbedding a serie of HGFs in a graphical model
    WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
-I0000 00:00:1701338396.373773    3481 tfrt_cpu_pjrt_client.cc:349] TfrtCpuClient created.
+I0000 00:00:1701339093.638650    3478 tfrt_cpu_pjrt_client.cc:349] TfrtCpuClient created.
 No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)
    @@ -623,9 +623,9 @@

    Embedding a serie of HGFs in a graphical model
    - 100.00% [4000/4000 00:25<00:00 Sampling 2 chains, 0 divergences] + 100.00% [4000/4000 00:26<00:00 Sampling 2 chains, 0 divergences]
    - 
    Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 26 seconds.
+    
    Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 27 seconds.
 
    We recommend running at least 4 chains for robust computation of convergence diagnostics
@@ -641,7 +641,7 @@ 
    Embedding a serie of HGFs in a graphical model
-../_images/1d41e1af7b77713d9cee1ff0ec1e42b6b992e55cc0d400ab33b3c9df96353a5e.png
+../_images/0000355356db0998c2f3f0a09396346e76affc88e942a2b29773199091397131.png
    @@ -683,74 +683,74 @@

    Embedding a serie of HGFs in a graphical model

    diff --git a/notebooks/Example_1_Heart_rate_variability.html b/notebooks/Example_1_Heart_rate_variability.html index 86a705088..9f49c52dd 100644 --- a/notebooks/Example_1_Heart_rate_variability.html +++ b/notebooks/Example_1_Heart_rate_variability.html @@ -815,16 +815,16 @@

    Loading and preprocessing physiological recording
    Downloading ECG channel:   0%|          | 0/2 [00:00<?, ?it/s]

    -
    Downloading ECG channel:  50%|█████     | 1/2 [00:01<00:01,  1.40s/it]
+
    Downloading ECG channel:  50%|█████     | 1/2 [00:00<00:00,  1.34it/s]
 
    
 
    
-
    Downloading Respiration channel:  50%|█████     | 1/2 [00:01<00:01,  1.40s/it]
+
    Downloading Respiration channel:  50%|█████     | 1/2 [00:00<00:00,  1.34it/s]
 
    
 
    
-
    Downloading Respiration channel: 100%|██████████| 2/2 [00:02<00:00,  1.51s/it]
+
    Downloading Respiration channel: 100%|██████████| 2/2 [00:01<00:00,  1.41it/s]
 
    
 
    
-
    Downloading Respiration channel: 100%|██████████| 2/2 [00:02<00:00,  1.50s/it]
+
    Downloading Respiration channel: 100%|██████████| 2/2 [00:01<00:00,  1.40it/s]
 
    
 
    
 
    
@@ -849,12 +849,12 @@ 
    Plot the signal with instantaneous heart rate derivations
    
+